Deciphering Systemic Sclerosis Phenotypes: A Novel Approach Using Clustering Algorithms and Proteomic Insights. Results from the PRECISESADS Study.

Deciphering Systemic Sclerosis Phenotypes: A Novel Approach Using Clustering Algorithms and Proteomic Insights. 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Results from the PRECISESADS Study. Santiago Dans Caballero, Rafaela Ortega-Castro, Chary López-Pedrera, and 4 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7109703/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 28 Nov, 2025 Read the published version in Journal of Translational Medicine → Version 1 posted 5 You are reading this latest preprint version Abstract Background Systemic sclerosis (SSc) is a heterogeneous autoimmune disease with high mortality driven by multiorgan involvement and limited therapeutic options. Traditional classifications based on skin involvement or serology are insufficient to capture disease complexity or predict outcomes accurately. Objective To identify clinically and molecularly distinct subtypes of SSc using unsupervised clustering and proteomic profiling. Methods K means clustering was applied to clinical and serological data from 402 SSc patients in the PRECISESADS cohort. The resulting clusters were validated in an independent local cohort (n = 213). To explore molecular differences, a random subset of 154 PRECISESADS patients underwent serum proteomic profiling using a panel of 92 organ damage–related proteins. Functional relevance was further investigated by exposing dermal fibroblasts to patient serum and assessing gene expression. Results Two distinct clusters were identified and validated, differing in organ involvement and autoantibody profiles. Cluster 2 was associated with more severe disease, including higher prevalence of ILD, PAH, and musculoskeletal manifestations, and enriched in anti-Scl-70 antibodies. Proteomic analysis revealed upregulation of 26 proteins in Cluster 2, related to fibrosis, inflammation, and endothelial dysfunction. Serum from these patients induced the in vitro expression of pro-fibrotic and inflammatory genes in fibroblasts. Altered levels of several proteins also correlated with relevant clinical features, suggesting potential biomarker utility. Conclusion Unsupervised clustering and proteomic profiling reveal biologically distinct subgroups within SSc, beyond traditional clinical or serological classifications. Our findings support the integration of molecular tools into patient stratification strategies, paving the way toward personalized medicine in SSc. Figures Figure 1 Figure 2 Figure 3 Figure 4 1. Introduction Systemic sclerosis (SSc) is a rare systemic autoimmune disease characterized by progressive multiorgan involvement. Although its pathophysiology is not fully understood, key mechanisms include immune dysregulation, progressive fibrosis, and vasculopathy, mainly affecting the microvasculature 1 . Clinically, SSc exhibits significant heterogeneity, ranging from mild cutaneous or vascular manifestations to rapidly progressive multiorgan damage. Among autoimmune rheumatic diseases, it has the highest disease-specific mortality rate 2 , largely driven by disease progression and the lack of effective disease-modifying therapies 3 . Despite advances in our understanding of SSc over the past decades, its mortality rate remains unacceptably high, with only marginal improvements observed over the last 40 years 4 . Several factors have been associated with increased mortality, including male sex, the diffuse cutaneous phenotype, and cumulative organ damage, such as interstitial lung disease (ILD) and pulmonary arterial hypertension (PAH) 5 . Tools like the Scleroderma Clinical Trials Consortium Damage Index (SCTC-DI) have shown prognostic value but remain limited in clinical use 6 . Accurate stratification of SSc patients is crucial for the early identification of individuals at higher risk of developing organ damage, as well as for potential preventive interventions. Traditionally, SSc patients have been classified based on the extent of skin sclerosis ( sine , limited, or diffuse) following the description by LeRoy et al. in 1988 7 . This classification gained widespread clinical acceptance due to its ease of use, requiring no additional tools beyond the Modified Rodnan Skin Score (mRSS) 8 , and it remains extensively used today. However, significant limitations of this classification have recently emerged 9 , as it may not adequately reflect cumulative organ damage in certain patient subgroups. Additional criticisms of this model include the dynamic nature of skin involvement, the high interobserver variability, and the poor prognosis observed in a subset of patients despite having a limited cutaneous phenotype 10 . Moreover, cutaneous involvement does not necessarily correlate with internal organ damage; patients with minimal skin sclerosis may still experience life-threatening complications 11 , underscoring the need for alternative classification methods. Since autoimmunity is a key process in the pathophysiology of SSc, autoantibodies play a fundamental role in the disease. Antinuclear antibodies (ANAs) are present in over 90% of patients, though their absence does not exclude diagnosis 12 . A set of nearly mutually exclusive 13 SSc-specific autoantibodies (such as anti-Scl-70, anti-centromere (ACA), and anti-RNA polymerase III) has proven useful for patient stratification and is included in classification criteria 14 . ACA typically correlates with the limited cutaneous form and lower risk of severe organ involvement, whereas anti-Scl-70 and anti-RNA polymerase III, particularly the latter, are associated with diffuse skin involvement, more aggressive disease, and higher mortality. Additional autoantibodies, including non-specific ones like anti-Ro-52 15 , have been linked to complications such as ILD. Emerging markers like anti-eIF2B 16 have been identified in a subset of seronegative patients (~ 10%), although their clinical relevance remains unclear. Given these findings, some authors have proposed an alternative classification model based on autoantibody profile rather than cutaneous phenotype 17 . This hypothesis was recently tested in a French cohort, demonstrating improved mortality prediction 18 . However, important gaps remain in risk stratification, as some patients with autoantibodies typically linked to milder phenotypes still experience poor outcomes 11 . While autoantibody-based models enhance risk prediction, they do not fully reflect disease heterogeneity, particularly in cases with overlapping features or undefined serological profiles. However, autoantibodies alone may not predict disease trajectory, since genetic, environmental, and epigenetic factors also contribute to progression 19 . These limitations highlight the need to refine phenotyping strategies. The increasing recognition of the complexity of SSc has driven the incorporation of molecular tools into patient stratification. Recent studies have demonstrated that integrating transcriptomic and proteomic profiling can identify distinct molecular signatures, enabling a more precise and personalized classification of the disease. In particular, proteomics has emerged as a promising tool for detecting key biomarkers associated with organ damage progression, facilitating the identification of subgroups with different prognoses and therapeutic responses 20,21 . These approaches represent a paradigm shift in SSc classification, moving away from models based solely on clinical and serological manifestations toward integrative systems that combine clinical, immunological, and molecular data. Importantly, such strategies also help overcome the limitations of current animal models, which often fail to reproduce the full spectrum of immune, vascular, and fibrotic manifestations observed in human SSc, thus bridging a critical gap between preclinical research and clinical application 22 . However, the clinical implementation of these findings remains challenging, as further validation is required to translate these advancements into routine medical practice. The objective of our study is to explore new approaches for the accurate stratification and characterization of SSc patients. We aim to achieve this by employing an unsupervised clustering algorithm and proteomic analysis to detect key protein expression differences pivotal for understanding the disease’s pathophysiology. 2. Methods Study Design and Population This observational, cross-sectional study was conducted using the PRECISESADS cohort, a multicenter European initiative involving 19 centers across 9 countries (Austria, Belgium, France, Germany, Hungary, Italy, Portugal, Spain, and Switzerland). The cohort enrolled patients with systemic autoimmune diseases (SADs) aged over 18, recruited from 2014 to 2019. Biological samples and data from electronic medical records were collected from all patients. The study protocol is registered on ClinicalTrials.gov under the identifier NCT02890126 23 . A total of 402 patients diagnosed with SSc who met the 2013 classification criteria of the American College of Rheumatology (ACR)/European Alliance of Associations for Rheumatology (EULAR) 14 were included in this study. In addition, an independent validation cohort comprising 213 patients with SSc was recruited at Reina Sofia University Hospital (Cordoba, Spain). This local cohort was used to replicate and validate key findings derived from the PRECISESADS dataset. Exclusion criteria comprised the concomitant diagnosis of another SAD, the use of a combination of two or more immunosuppressive agents, high-dose corticosteroid therapy in the three months prior to recruitment (≥ 15 mg/day), chronic infections, or participation in experimental therapies. All participants were fully informed about the study objectives and provided written informed consent prior to inclusion. The study adhered to ethical standards and was approved by the local research ethics committees of the participating centers, including Reina Sofia University Hospital (approval number 5995). Variables and Clinical Data Collection Common variables recorded for all SADs included sex, age at onset, ethnicity, disease duration since diagnosis, and treatment. For SSc, a total of 87 clinically relevant variables were collected throughout the PRECISESADS project, from which a subset was selected for the present analysis (Supplementary Table 1). These included antibody status, cutaneous phenotype, and organ involvement (e.g., PAH, ILD, or esophageal dysmotility). Clustering Analysis Patients were stratified into distinct subgroups using an unsupervised k-means clustering algorithm applied to a curated selection of clinical and serological variables. Variable inclusion was guided by clinical expertise and their established relevance to key pathophysiological features of systemic sclerosis, prioritizing parameters associated with disease severity and major organ involvement. Only variables with complete data were included in the clustering analysis. The optimal number of clusters was determined using the NbClust package in R, which evaluates 23 internal validation indices. Among these, six supported a two-cluster solution, including the Calinski-Harabasz, Silhouette, Duda, Pseudo T², Beale, and McClain indices. This consensus supports the selection of k = 2 as the most parsimonious and biologically coherent solution. Although alternative configurations (e.g., k = 3, 4, or 5) were also explored, they yielded less distinct or clinically non-informative partitions. This two-cluster solution was subsequently validated in the independent local cohort, confirming its robustness and clinical relevance. For the clustering analysis, the following R packages were utilized: stats (for k-means clustering), NbClust (for cluster validation), cluster (for silhouette width calculation), and factoextra (for visualization). Proteomic Analysis Following cluster identification, a representative subset of patients from each cluster within the original PRECISESADS cohort was randomly selected for proteomic profiling using OLINK® technology (Uppsala, Sweden), a high-throughput platform for multiplexed protein quantification. A panel of 92 proteins, selected for their relevance to organ damage, was analyzed. Differential protein expression between clusters was assessed using volcano plots and principal component analysis (PCA) to visualize clustering patterns and separation. A complete list of the 92 proteins analyzed is available in Supplementary Table 2. Statistically significant proteins (with a controlled false discovery rate, FDR) were further evaluated using functional enrichment analyses via STRING-db.org to explore the implicated cellular pathways. In Vitro Experiments An in vitro experiment was conducted using fibroblast cells, which play a key role in the pathogenesis of SSc. Dermal fibroblasts were immortalized in-house by overexpression of SV40, generating the human primary transformed cell line FibSV40. Cells were cultured in DMEM medium supplemented with 10% heat-inactivated fetal bovine serum (FBS) and 1% antibiotic-antimycotic solution, with medium replacement every 48 hours until 80% confluence was reached. Cultures were maintained in an incubator at 37°C with 5% CO₂ (Thermo Scientific, Series 8000 DH, Illinois, USA). To evaluate the impact of the altered circulating profiles in SSc, fibroblasts were treated for 24 hours with 15% serum from eighteen SSc patients (nine from each of the two previously identified molecular clusters matched for disease duration, age and sex). Following incubation, fibroblasts were washed three times with PBS before RNA extraction to eliminate extracellular nucleic acids and avoid serum-derived mRNA contamination. Subsequently, several markers of fibrosis and inflammation were analyzed using RT-qPCR. RNA Isolation and Transcriptomic Analyses Expression levels of selected genes related to fibrosis and inflammation (including PDGFRB, WNT3A, MMP2, CCL3, TNFα, CCL5, and IL-1B) were analyzed in fibroblasts by quantitative real-time reverse transcription PCR (RT-qPCR). RNA was extracted from fibroblast samples by homogenization in TRIzol™ (Invitrogen), followed by phase separation with chloroform, as previously described 24 . Reverse transcription was performed using the PrimeScript RT Master Mix Kit (Takara, Lisbon, Portugal), according to the manufacturer’s instructions. qPCR was carried out using a LightCycler thermocycler system (Roche Diagnostics, Indianapolis, USA) and SYBR® Green (Promega Biotech, Madrid, Spain). Primer sequences (Merck KGaA, Darmstadt, Germany) are listed in Supplementary Table 1. Gene expression levels were normalized using the geometric mean of α-actin (ACT) and glyceraldehyde-3-phosphate dehydrogenase (GAPDH). Data analysis was performed using the 2^-ΔΔCt method. Statistical Analysis Data were analyzed using RStudio v.2024.09. Descriptive statistics were reported as mean and standard deviation for continuous variables, and as frequencies and percentages for categorical variables. Group comparisons were performed using the Student’s t-test for continuous variables and the chi-square test for categorical variables. A bidirectional approach was applied to all statistical models. The threshold for statistical significance was set at p < 0.05, and the Benjamini-Hochberg procedure was applied to control the FDR due to multiple testing. 3. Results Unsupervised clustering analysis revealed two distinct clinical subgroups among SSc patients A total of 402 patients from the PRECISESADS cohort were included in the analysis. The cohort was predominantly female (84.3%), with a mean age of 58.1 ± 12.9 years and a mean disease duration of 10.8 ± 8.7 years at enrollment. An unsupervised clustering algorithm applied to clinical and serological variables identified two distinct patient subgroups (Table 1 ). Cluster 1 (n = 221) and Cluster 2 (n = 181) differed significantly in clinical features, autoantibody profiles, and the extent of organ involvement, underscoring the heterogeneity of SSc. Cluster 2 exhibited a higher burden of severe organ involvement, with significantly increased prevalence of interstitial lung disease (ILD, 59.7% vs. 14.5%), pulmonary arterial hypertension (PAH, 30.9% vs. 9%), and sclerodactyly (93.9% vs. 53.4%) (all p < 0.01). Musculoskeletal and vascular complications were also more frequent in Cluster 2, including arthritis (39.8% vs. 20.8%), calcinosis (43.1% vs. 6.8%), digital ulcers (15.5% vs. 5%), telangiectasias (83.4% vs. 39.8%), pitting scars (73.5% vs. 29.4%), and muscle weakness (28.2% vs. 9.5%) (p < 0.01 for all). Limited cutaneous phenotype was more prevalent in Cluster 1 (60.2% vs. 45.9%, p < 0.01), suggesting more extensive skin involvement in Cluster 2. No significant differences were found in age (59.2 ± 12.4 vs. 57.2 ± 13.2 years, p = 0.12) or disease duration (11.6 ± 7.5 vs. 10.2 ± 9.6 years, p = 0.10), indicating that the observed divergence reflects distinct disease phenotypes rather than demographic or temporal factors. Autoantibody profiles also differentiated the clusters. Anti-Scl-70 antibodies were more frequent in Cluster 2 (38.7% vs. 19.9%, p < 0.01), consistent with their association with severe disease. Conversely, anti-centromere antibodies were more prevalent in Cluster 1 (43.4% vs. 29.3%, p < 0.01), though notably, around 30% of patients in Cluster 2 also expressed them, highlighting the limitations of classification based solely on autoantibodies. These findings were partially replicated in an independent cohort of 213 patients from Reina Sofía University Hospital (Table 2 ). While some clinical patterns were preserved, differences in disease duration and organ involvement were observed. Thus, this analysis should be considered an exploratory replication supporting the generalizability of the clustering structure. The identified clinical subgroups of SSc patients also displayed distinct patterns of proteins associated with organ damage. To explore the molecular basis underlying the clinical clusters, serum proteomic profiling was performed in a random subset of 154 patients (77 per cluster), using a panel of 92 organ damage-related proteins quantified by Proximity Extension Assay (PEA) technology. The selected patients mirrored the clinical features of each cluster. Principal component analysis (PCA) revealed partial segregation of the two clusters based on proteomic profiles (Fig. 1 A). Despite overall correlation across samples, distinct molecular patterns emerged (Fig. 1 B). Cluster 2 exhibited upregulated levels of NOS3, PON2, MAP4K5, AIFM1, PRKAB1, NUB1, STX8, and PDGFC, and downregulation of HPGDS compared to Cluster 1 (Fig. 1 C, D). These proteins are involved in inflammation, oxidative stress, endothelial dysfunction, metabolism, and fibrosis (hallmarks of SSc pathogenesis) and their dysregulation suggests a more aggressive molecular phenotype in Cluster 2. The clinical cluster with the most severe phenotype exhibited the strongest molecular alterations at the protein level. To gain further insight into these molecular alterations, we conducted a comparative analysis of organ damage-related protein levels in the identified clusters versus 17 healthy donors, aiming to identify proteins that are altered in these patient groups. Compared to healthy controls, Cluster 1 showed upregulation of only eight proteins (Fig. 2 A), whereas Cluster 2 exhibited a markedly broader proteomic dysregulation, with 26 proteins upregulated (Fig. 2 B). These findings further suggest that although molecular changes are present in all SSc patients, those in Cluster 2 exhibit a more pronounced proteomic signature that aligns with their more severe clinical manifestations. We also examined the overlap of deregulated proteins in Cluster 1 and Cluster 2 compared to healthy donors. All eight proteins altered in Cluster 1 (PGF, RRM2B, YES1, ENAH, CLEC1A, NOS3, EGFL7, and SERPINA9) were also dysregulated in Cluster 2, suggesting a key role in SSc pathogenesis. Moreover, Cluster 2 exhibited altered expression of an additional 18 unique proteins (Fig. 2 C). We also performed a pathway enrichment analysis of the altered protein signature in SSc to gain further insights. This analysis revealed an interactive protein network (Fig. 2 D) primarily associated with receptor tyrosine Kinases signaling (RTK) (FDR = 0.0091, signal 0.63) and broader signal transduction pathways (FDR = 0.0091, signal 0.43) (Fig. 3 D). The RTK pathway is critical in multiple biological processes, including fibrosis, immune activation, and endothelial dysfunction, all of which are key contributors to SSc pathogenesis. The dysregulation observed in Cluster 2 suggests that targeting this pathway could represent a potential therapeutic strategy for patients with severe disease phenotypes. Serum from the most severe clinical and molecular subgroup of SSc patients induced the expression of fibrotic and inflammatory genes in fibroblasts in vitro. To further explore the functional relevance of these proteomic alterations, an in vitro experiment was conducted using immortalized dermal fibroblasts. These fibroblasts were cultured in media containing serum from randomly selected patients from each cluster to assess their response to distinct molecular environments (Fig. 3 A). The objective was to determine whether the identified molecular differences translate into functional alterations in fibroblast activation. Serum of patients from Cluster 2 promoted the upregulation of fibrotic-related genes including PDGFRB, WNT3A and MMP2 (Fig. 3 B) as well as several genes associated with inflammation such as CCL3, TNF, CCL5 and IL1B (Fig. 3 C). This experiment provided additional validation that the proteomic profiles observed in Cluster 2 are functionally relevant, influencing fibroblast behavior in a manner consistent with the more severe fibrotic and inflammatory phenotype observed in these patients. Circulating levels of specific organ damage-related proteins were directly associated with key clinical features in SSc patients. In addition to cluster-specific proteomic differences, a final exploratory analysis was performed across all patients (regardless of clustering) to assess associations between protein expression and specific clinical manifestations, aiming to identify novel disease biomarkers and potential therapeutic targets (Fig. 4 ). Anti-Scl-70-positive patients exhibited increased levels of BANK1, BID, and ERBB2IP (Fig. 4 A), reinforcing their link to immune activation and apoptosis. Anti-centromere-positive patients showed altered expression of ATP6AP2, LTA4H, and DPP6 (Fig. 4 B), proteins involved in immune regulation and vascular homeostasis. Patients with pulmonary arterial hypertension (PAH) demonstrated elevated expression of AGR2, BAMBI, CALCA, EPO, and HPGDS (Fig. 4 D), proteins implicated in pulmonary vascular remodeling, endothelial dysfunction, and hypoxia response. Similarly, esophageal dysmotility was associated with upregulation of proteins related to neuronal signaling and fibrosis, including FKBPLB and PRKAB1 (Fig. 4 E), suggesting a potential role in neuromuscular dysfunction. All protein-clinical variable associations described were statistically significant. 4. Discussion Our study identifies two clinically and molecularly distinct subgroups of patients with SSc, providing a foundation for reclassifying this heterogeneous disease beyond traditional clinical phenotypes. One of the subgroups (Cluster 2) exhibits a significantly more severe phenotype characterized by extensive organ involvement, distinct autoantibody profiles, and a deeper and more complex proteomic dysregulation. These findings reinforce the growing need to transition from descriptive to mechanism-based classification systems in SSc. Clinical and Biological Interpretation of the Identified Clusters The unsupervised clustering approach, validated in an independent local cohort, robustly distinguished two subgroups of SSc patients with divergent clinical trajectories. Cluster 2, in particular, was characterized by a markedly increased burden of pulmonary (ILD and PAH), vascular (digital ulcers, telangiectasias), gastrointestinal (esophageal dysmotility), or musculoskeletal involvement (arthritis, calcinosis, muscle weakness), suggesting a more aggressive systemic disease phenotype. Interestingly, these differences were not accounted for by age or disease duration, indicating that they are likely driven by intrinsic molecular mechanisms rather than temporal disease evolution. This observation supports the existence of distinct biological subtypes within SSc, reinforcing the concept that clinical heterogeneity arises from specific underlying pathophysiological programs. The proteomic profile of Cluster 2 further substantiates this interpretation. Patients in this subgroup exhibited upregulation of proteins implicated in fibrosis (e.g., PDGFC, PRKAB1) 25,26 , oxidative stress (e.g., PON2, MAP4K5) 27,28 , endothelial dysfunction (e.g., NOS3) 29 , and immune activation (e.g., AIFM1, NUB1) 30,31 . These pathways are well-established contributors to SSc pathogenesis, and their coordinated dysregulation in Cluster 2 highlights a molecularly active, pro-fibrotic and inflammatory disease state. In contrast, Cluster 1, while not devoid of disease manifestations, showed a more limited proteomic disturbance, consistent with a milder clinical phenotype. Moreover, the clustering results support the concept that molecular heterogeneity in SSc may be rooted in early vascular dysfunction, as previously hypothesized. The interplay between endothelial injury, immune activation, and fibrotic remodeling appears central to the aggressive phenotype observed in Cluster 2. Shared and Unique Biomarkers: Implications for Disease Stratification A notable finding of our study is the identification of a core set of eight proteins commonly dysregulated in both clusters when compared to healthy controls. These include PGF, RRM2B, YES1, ENAH, CLEC1A, NOS3, EGFL7, and SERPINA9, and may represent a fundamental pathogenic axis in SSc. Their persistent alteration across patient subtypes suggests their potential utility as universal biomarkers of disease presence or activity. In contrast, 18 proteins uniquely upregulated in Cluster 2 provide insight into disease severity and progression. Among them, LTA4H stands out for its markedly increased expression and known role in promoting fibrosis through leukotriene B4 (LTB4) signaling, as supported by recent preclinical data 33 . In that study, elevated LTB4 levels were found in patients with SSc (particularly those with ILD or diffuse cutaneous forms) and blockade of the LTB4–BLT1 axis significantly reduced fibrosis in murine models. Mechanistically, LTB4 promoted myofibroblast and endothelial-to-mesenchymal transitions via PI3K/Akt/mTOR activation, independently of TGF-β. This protein, along with PRKAB1, MAP4K5, and PDGFC, may link inflammation, vascular dysfunction, and fibroblast activation in SSc. Functional Validation: From Proteomics to Pathogenic Mechanisms To determine whether the proteomic differences observed between clusters translated into functional consequences, we performed in vitro stimulation of dermal fibroblasts with serum from patients in each cluster. Fibroblasts exposed to serum from Cluster 2 patients exhibited increased expression of pro-fibrotic genes (e.g., PDGFRB, WNT3A, MMP2) 35,36 and inflammatory mediators (e.g., CCL3, TNF, CCL5, IL1B) 37,38 , confirming that the circulating molecular milieu in these individuals is capable of actively modulating fibroblast behavior. These findings functionally validate the proteomic alterations observed in Cluster 2, linking them to fibroblast activation and key mechanisms of tissue remodeling and damage. This reinforces the biological relevance of the identified protein signatures and suggests that they are not mere bystanders but active participants in the pathogenesis of the fibrotic phenotype. Mechanistic Insights and Therapeutic Implications Pathway enrichment analysis revealed a significant involvement of receptor tyrosine kinase (RTK)-related signaling in the protein network upregulated in Cluster 2. RTKs regulate cellular growth, differentiation, and extracellular matrix production, and their dysregulation has been implicated in various fibrotic and autoimmune disorders 39 . The enrichment of RTK components supports the hypothesis that this axis may represent a convergent pathogenic mechanism in severe SSc, integrating inflammatory, vascular, and fibrotic cues. In addition, several dysregulated proteins in Cluster 2 (e.g., MAP4K5, PRKAB1, AIFM1) are linked to intracellular signaling pathways involved in inflammation and fibrosis. These findings highlight the therapeutic relevance of intracellular kinase inhibitors, such as JAK inhibitors, which modulate cytokine-driven pathways, including those downstream of type I interferons 40 . Through mechanisms such as TGF-β inhibition, suppression of myofibroblast proliferation, macrophage modulation, and epigenetic regulation, JAK inhibitors have been proposed for patients with fibrotic phenotypes 41 . While none are currently approved for SSc, emerging evidence supports their use in selected profiles, particularly those with ILD 42 . Future trials should incorporate molecular stratification at the design phase, as uniform recruitment may dilute efficacy signals in heterogeneous populations. Interferons activate intracellular signaling via the JAK/STAT pathway 39 and have been extensively studied in SSc. Most patients exhibit an “interferon signature,” based on ISG overexpression in affected tissues 43,44 . Whether this signature is causal or reflective of disease remains unclear, but recent studies suggest it associates with more severe phenotypes 45 . A phase I trial of anifrolumab, an anti-IFNAR1 antibody, showed reductions in interferon-inducible proteins in SSc patients 46 . A phase III trial is ongoing and may expand therapeutic options 47 . Given the prominent interferon-related activation in Cluster 2, this axis represents a promising target in patients with inflammatory molecular signatures. Given the prominent activation of immune-related and interferon-associated proteins in Cluster 2, targeting this axis may hold particular promise in the subset of patients characterized by active inflammatory molecular signatures. Additionally, Cluster 2 showed enrichment of proteins involved in apoptosis resistance and mitochondrial stress, including BID, AIFM1, and ERBB2IP 48 . These may contribute to persistence of activated myofibroblasts, a hallmark of fibrosis 49 . Therapies aimed at restoring apoptotic signaling or selectively targeting profibrotic fibroblast subsets warrant further exploration 50 . Toward Precision Medicine in SSc The clinical and molecular heterogeneity of SSc has historically posed challenges for therapeutic development and disease management. Our findings suggest that integration of proteomic data with clinical phenotyping can enhance disease stratification and inform personalized approaches to care. For example, the strong associations between specific protein profiles and organ manifestations (e.g., HPGDS and PAH; PRKAB1 and esophageal dysmotility) highlight the potential for using serum biomarkers to guide monitoring and predict complications. Moreover, the ability to define biologically meaningful clusters opens avenues for tailored therapeutic decision-making. Patients in Cluster 2 may benefit from early, aggressive intervention and closer monitoring due to their elevated molecular activity, while those in Cluster 1 could follow a more conservative approach. This proteomics-driven stratification represents a step toward precision medicine in SSc. To our knowledge, this is the first study to perform unsupervised clustering of patients with SSc based exclusively on extended clinical data (beyond the limited/diffuse cutaneous classification) and to subsequently assess their molecular profiles. This strategy enabled the identification of patient subgroups that are not only clinically distinct but also show meaningful differences at the molecular level. Our findings contribute to the growing body of evidence highlighting the limitations of the traditional binary classification, which fails to capture the full spectrum of clinical trajectories and therapeutic responses in SSc 9–11 . A large multicenter analysis based on the EUSTAR cohort previously applied cluster analysis to clinical data and identified phenotypic subsets with prognostic relevance, yet without incorporating molecular data 51 . In parallel, molecular profiling studies (particularly transcriptomic analyses of skin biopsies) have defined subsets characterized by dominant fibrotic, interferon, or vascular signatures 21,52,53 , which often transcend conventional clinical phenotypes. Additionally, longitudinal studies have identified molecular features associated with early versus late disease 53 and with progression from preclinical to overt SSc 54 . Our study bridges these lines of evidence by showing that clinically derived clusters also reflect distinct molecular programs. Notably, we provide functional validation: serum from patients in the more severe cluster induces fibrotic and inflammatory gene expression in dermal fibroblasts, indicating that these clusters represent active pathogenic states rather than statistical groupings, consistent with prior studies demonstrating that molecular profiles can correspond to biologically active and clinically relevant pathways 55 . Altogether, our results support the integration of clinical and molecular data to refine patient stratification and guide personalized therapeutic approaches in SSc. Study Limitations and Future Directions Despite robust findings, several limitations should be noted. First, the proteomic analysis was limited to a targeted panel of 92 proteins, potentially overlooking relevant pathways. Future studies should incorporate broader omics approaches (e.g., transcriptomics, metabolomics, single-cell analyses) to capture the full spectrum of disease heterogeneity. Second, external validation in larger and multi-ethnic cohorts is needed to ensure generalizability. Longitudinal studies will help assess the predictive value of identified markers for progression and therapeutic response. Importantly, integration of molecular stratification into prospective clinical trials is crucial to determine its utility in guiding treatment decisions. In vivo validation of key targets (e.g., LTA4H, PRKAB1, AIFM1) will be essential to translate discovery into therapy. Multidisciplinary collaboration and international efforts remain key to overcoming current barriers in SSc research and care. 5. Conclusion Altogether, our findings support the existence of distinct clinical and molecular subtypes within SSc, with potential implications for diagnosis, prognosis, and targeted therapy. The integration of unsupervised clinical clustering with serum proteomics and functional validation offers a powerful framework to unravel the complexity of SSc. This approach paves the way toward biomarker-driven stratification and precision medicine in a disease that remains challenging to treat. Future efforts should focus on expanding molecular profiling, validating key markers, and translating these insights into actionable clinical tools. Declarations Collaborators : PRECISESADS Clinical Consortium: Lorenzo Beretta, Barbara Vigone, Jacques-Olivier Pers, Alain Saraux, Valérie Devauchelle-Pensec, Divi Cornec, Sandrine Jousse-Joulin, Bernard Lauwerys, Julie Ducreux, Anne-Lise Maudoux, Carlos Vasconcelos, Ana Tavares, Esmeralda Neves, Raquel Faria, Mariana Brandão, Ana Campar, António Marinho, Fátima Farinha, Isabel Almeida, Miguel Angel Gonzalez-Gay Mantecón, Ricardo Blanco Alonso, Alfonso Corrales Martínez, Ricard Cervera, Ignasi Rodríguez-Pintó, Gerard Espinosa, Rik Lories, Ellen De Langhe, Nicolas Hunzelmann, Doreen Belz, Torsten Witte, Niklas Baerlecken, Georg Stummvoll, Michael Zauner, Michaela Lehner, Eduardo Collantes, Rafaela Ortega-Castro, Ma Angeles Aguirre-Zamorano, Alejandro Escudero-Contreras, Ma Carmen Castro-Villegas, Norberto Ortego, María Concepción Fernández Roldán, Enrique Raya, Inmaculada Jiménez Moleón, Enrique de Ramon, Isabel Díaz Quintero, Pier Luigi Meroni, Maria Gerosa, Tommaso Schioppo, Carolina Artusi, Carlo Chizzolini, Aleksandra Zuber, Donatienne Wynar, Laszló Kovács, Attila Balog, Magdolna Deák, Márta Bocskai, Sonja Dulic, Gabriella Kádár, Falk Hiepe, Velia Gerl, Silvia Thiel, Manuel Rodriguez Maresca, Antonio López-Berrio, Rocío Aguilar-Quesada, Héctor Navarro-Linares Funding statement : This manuscript was made possible thanks to the support of the Andalusian Foundation for Rheumatology through grant FAR-2024-001 (SCLEROMIC project). Disclosure of conflicts of interest : The authors declare no conflict of interest. Acknowledgements We sincerely thank Professor Eduardo Muñoz’s laboratory at the Maimonides Biomedical Research Institute of Cordoba (IMIBIC) for kindly providing the fibroblast cultures, without which the in vitro experiments would not have been possible. Authors contribution : * These authors share equal contribution. The authors confirm contribution to the paper as follows: Santiago Dans-Caballero : Investigation. Writing – Original Draft. Rafaela Ortega-Castro : Writing – Review & Editing. Resources. Conceptualization. Supervision. Beatriz Vellón-García: Writing – Review & Editing. Resources. Alejandro Escudero-Contreras: Writing – Review & Editing. Supervision. Chary López-Pedrera : Writing – Review & Editing. Resources. Conceptualization. Supervision. Carlos Pérez-Sánchez: Writing – Review & Editing. Resources. Conceptualization. Supervision. Clementina López-Medina : Writing – Review & Editing. Resources. Conceptualization. Supervision. Declaration of generative AI and AI-assisted technologies in the writing process During the preparation of this work, the authors used ChatGPT (OpenAI, San Francisco, CA) in order to assist with language editing, style refinement, and structural suggestions for improved clarity and readability. After using this tool, the authors reviewed and edited the content as needed and take full responsibility for the content of the publication. References Volkmann ER, Andréasson K, Smith V. Systemic sclerosis. Lancet. 2022 Nov 25;401:304–18. doi: 10.1016/S0140-6736(22)01692-0. Bournia VK, Fragoulis GE, Mitrou P, Mathioudakis K, Tsolakidis A, Tektonidou MG, et al. All-cause mortality in systemic rheumatic diseases under treatment compared with the general population, 2015-2019. RMD Open. 2021 Nov;7(3):e001694. doi: 10.1136/rmdopen-2021-001694. Del Galdo F, Lescoat A, Conaghan PG , Bertoldo E, Čolić J, Santiago T et al. 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Ann Rheum Dis. 2023;82(12):1513–22. doi:10.1136/ard-2022-223986. Bellocchi C, Beretta L, Wang X, Lyons MA, Marchini M, Lorini M, et al. Longitudinal global transcriptomic profiling of preclinical systemic sclerosis reveals molecular changes associated with disease progression. Rheumatology (Oxford). 2023;62(4):1662–8. doi:10.1093/rheumatology/keac492. Wermuth PJ, Piera-Velazquez S, Jimenez SA. Exosomes isolated from serum of systemic sclerosis patients display alterations in their content of profibrotic and antifibrotic microRNA and induce a profibrotic phenotype in cultured normal dermal fibroblasts. Clin Exp Rheumatol. 2017;35 Suppl 106(4):21–30. Tables Table 1 Comparison of clinical and serological characteristics between the two identified clusters of SSc patients (PRECISESADS cohort). ¶: variables included in the clustering algorithm. ILD: interstitial lung disease; PAH: pulmonary arterial hypertension; GERD: gastroesophageal reflux disease; bDMARD: biologic disease-modifying antirheumatic drug. Variable PRECISESADS cohort (n = 402) Cluster 1 (n = 221) Cluster 2 (n = 181) p-value Demographic variables Age (years), mean (SD) ¶ 58.1 (12.9) 57.2 (13.2) 59.2 (12.4) 0.12 Disease duration (years), mean (SD) 10.8 (8.7) 10.2 (9.6) 11,6 (7.5) 0.10 Sex (female), n (%) ¶ 339 (84.3) 188 (85.1) 151 (83.4) 0.75 Clinical features Raynaud phenomenon, n (%) ¶ 389 (96.8) 209 (94.6) 180 (99.4) 0.01 Limited cutaneous phenotype, n (%) 216 (53.7) 133 (60.2) 83 (45.9) < 0.01 Sclerodactyly, n (%) ¶ 288 (71.6) 118 (53.4) 170 (93.9) < 0.01 ILD, n (%) ¶ 140 (34.8) 32 (14.5) 108 (59.7) < 0.01 PAH, n (%) ¶ 76 (18.9) 20 (9) 56 (30.9) < 0.01 Digital ulcers, n (%) ¶ 39 (9.7) 11 (5) 28 (15.5) < 0.01 Telangiectasias, n (%) ¶ 239 (59.5) 88 (39.8) 151 (83.4) < 0.01 Pitting scars, n (%) ¶ 198 (49.3) 65 (29.4) 133 (73.5) < 0.01 Puffy fingers, n (%) 225 (56) 116 (52.5) 109 (60.2) 0.14 Calcinosis, n (%) ¶ 93 (23.1) 15 (6.8) 78 (43.1) < 0.01 Arthritis, n (%) ¶ 118 (29.4) 46 (20.8) 72 (39.8) < 0.01 GERD, n (%) ¶ 267 (66.4) 117 (52.9) 150 (82.9) < 0.01 Esophageal dysmotility, n (%) ¶ 200 (49.7) 77 (63.6) 123 (68) < 0.01 Muscle weakness, n (%) ¶ 72 (17.9) 21 (9.5) 51 (28.2) < 0.01 Centromere positivity, n (%) 149 (37.1) 96 (43.4) 53 (29.3) < 0.01 Scl-70 positivity, n (%) 114 (28.4) 44 (19.9) 70 (38.7) < 0.01 Comorbidities Ever smoking, n (%) ¶ 55 (13.7) 32 (14.5) 23 (1.7) 0.71 Hypertension, n (%) 129 (32.1) 72 (32.6) 57 (31.5) 0.90 Dyslipidemia, n (%) 98 (24.4) 57 (25.8) 41 (22.6) 0.54 Obesity, n (%) 39 (9.7) 23 (10.4) 16 (8.8) 0.72 Treatments Current bDMARD, n (%) 10 (2.5) 2 (0.9) 8 (4.4) 0.04 Current immunosuppressants, n (%) 106 (26.4) 32 (14.5) 74 (40.9) < 0.01 Statins use, n (%) 84 (20.9) 42 (19) 42 (23.2) 0.36 Table 2 Comparison of clinical and serological characteristics between the two identified clusters of SSc patients (Reina Sofia University Hospital cohort). ILD: interstitial lung disease; PAH: pulmonary arterial hypertension; GERD: gastroesophageal reflux disease. Variable Local cohort (n = 213) Cluster 1 (n = 92) Cluster 2 (n = 121) p-valor Demographic variables Age (years), mean (SD) 62.9 (14.6) 61.8 (13.2) 64.9 (15.2) < 0.01 Disease duration (years), mean (SD) 10.5 (8.4) 8.3 (7.4) 12.9 (8.6) < 0.01 Sex (female), n (%) 187 (87.8) 81 (88) 106 (87.6) 1 Clinical features Raynaud phenomenon, n (%) 186 (87.3) 78 (84.8) 108 (89.3) 0.13 Limited cutaneous phenotype, n (%) 128 (60) 54 (58.7) 74 (61.2) 0.42 Sclerodactyly, n (%) 150 (70.4) 55 (59.8) 95 (78.5) < 0.01 ILD, n (%) 85 (39.9) 35 (38) 50 (41.3) 0.73 PAH, n (%) 43 (20.2) 10 (10.9) 33 (27.3) < 0.01 Digital ulcers, n (%) 68 (31.9) 22 (23.9) 46 (38) 0.04 Telangiectasias, n (%) 134 (62.9) 42 (45.7) 92 (76) < 0.01 Pitting scars, n (%) 76 (35.7) 30 (32.6) 46 (38) 0.50 Calcinosis, n (%) 35 (16.4) 12 (13) 23 (19) 0.33 Arthritis, n (%) 45 (21.1) 18 (19.6) 27 (22.3) 0.73 GERD, n (%) 106 (49.8) 42 (45.7) 64 (52.9) 0.33 Centromere positivity, n (%) 120 (56.3) 57 (62) 63 (52.1) 0.16 Scl-70 positivity, n (%) 31 (14.6) 10 (10.9) 21 (17.4) 0.25 Comorbidities Ever smoking, n (%) 56 (26.3) 26 (28.3) 30 (24.8) 0.85 Hypertension, n (%) 81 (38) 31 (33.7) 50 (41.3) 0.32 Dyslipidemia, n (%) 69 (32.4) 30 (32.6) 39 (32.2) 1 Statins use, n (%) 69 (32.4) 28 (30.4) 41 (33.9) 0.68 Supplementary Files SupplementaryMethods.docx SupplementaryTables.docx Cite Share Download PDF Status: Published Journal Publication published 28 Nov, 2025 Read the published version in Journal of Translational Medicine → Version 1 posted Editorial decision: Major revision 12 Sep, 2025 Reviewers agreed at journal 20 Aug, 2025 Reviewers invited by journal 20 Aug, 2025 Editor assigned by journal 14 Jul, 2025 First submitted to journal 12 Jul, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-7109703","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":503252769,"identity":"fe2e087a-64d7-4dd4-99ac-4caf65df1972","order_by":0,"name":"Santiago Dans Caballero","email":"data:image/png;base64,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","orcid":"https://orcid.org/0009-0002-8529-0125","institution":"Reina Sofia University Hospital: Hospital Universitario Reina Sofia","correspondingAuthor":true,"prefix":"","firstName":"Santiago","middleName":"Dans","lastName":"Caballero","suffix":""},{"id":503252770,"identity":"4d1b6097-1f20-48c6-abc6-42de68a0c91d","order_by":1,"name":"Rafaela Ortega-Castro","email":"","orcid":"","institution":"Reina Sofia University Hospital: Hospital Universitario Reina Sofia","correspondingAuthor":false,"prefix":"","firstName":"Rafaela","middleName":"","lastName":"Ortega-Castro","suffix":""},{"id":503252771,"identity":"c3a0f01d-f45a-430b-8e9d-7632511f747f","order_by":2,"name":"Chary López-Pedrera","email":"","orcid":"","institution":"IMIBIC: Instituto Maimonides de Investigacion Biomedica de Cordoba","correspondingAuthor":false,"prefix":"","firstName":"Chary","middleName":"","lastName":"López-Pedrera","suffix":""},{"id":503252772,"identity":"d84a6774-0177-4f11-90c7-bc39c41c4e21","order_by":3,"name":"Alejandro Escudero-Contreras","email":"","orcid":"","institution":"Reina Sofia University Hospital: Hospital Universitario Reina Sofia","correspondingAuthor":false,"prefix":"","firstName":"Alejandro","middleName":"","lastName":"Escudero-Contreras","suffix":""},{"id":503252773,"identity":"4d5e9b99-bcda-4018-9e9f-34225c23cfca","order_by":4,"name":"Beatriz Vellón-García","email":"","orcid":"","institution":"IMIBIC: Instituto Maimonides de Investigacion Biomedica de Cordoba","correspondingAuthor":false,"prefix":"","firstName":"Beatriz","middleName":"","lastName":"Vellón-García","suffix":""},{"id":503252774,"identity":"b0e65ee7-1b68-4655-8dde-32af6094b5f4","order_by":5,"name":"Carlos Pérez-Sánchez","email":"","orcid":"","institution":"IMIBIC: Instituto Maimonides de Investigacion Biomedica de Cordoba","correspondingAuthor":false,"prefix":"","firstName":"Carlos","middleName":"","lastName":"Pérez-Sánchez","suffix":""},{"id":503252775,"identity":"0a424919-14aa-4bdc-9d26-5aa2836d02de","order_by":6,"name":"Clementina López-Medina","email":"","orcid":"","institution":"Reina Sofia University Hospital: Hospital Universitario Reina Sofia","correspondingAuthor":false,"prefix":"","firstName":"Clementina","middleName":"","lastName":"López-Medina","suffix":""}],"badges":[],"createdAt":"2025-07-12 18:21:14","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7109703/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7109703/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1186/s12967-025-07469-0","type":"published","date":"2025-11-28T15:58:36+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":90311348,"identity":"156c3efd-0a27-424f-abc0-0b15bb61615e","added_by":"auto","created_at":"2025-09-01 09:49:49","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":662114,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eProteomic analysis of organ damage markers in SSc. (\u003c/strong\u003eA) Principal component analysis (PCA) of SSc patient clusters based on organ damage-related proteomic markers. (B) Heatmap showing correlations among protein levels across all patients. (C) Volcano plot of differentially expressed proteins between SSc clusters. (D) Circulating levels of statistically significant proteins differentiating cluster 1 and cluster 2.\u003c/p\u003e","description":"","filename":"fig1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7109703/v1/790c885c9af403093ab195cf.jpg"},{"id":90310118,"identity":"f60d8cdc-846e-431f-8fe1-d356db824afc","added_by":"auto","created_at":"2025-09-01 09:41:49","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":525646,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eComparative volcano plots and functional interaction network of differentially expressed proteins in SSc patient clusters vs healthy donors.\u003c/strong\u003e(A) Volcano plot comparing protein expression between healthy controls and patients in Cluster 1. (B) Volcano plot showing the comparison between healthy controls and Cluster 2 patients, with a greater number of significantly upregulated proteins. (C) Venn diagram showing the overlap of differentially expressed proteins in clusters 1 and 2 compared to healthy donors. (D, E) Functional interaction network of the differentially expressed proteins compared to healthy controls, constructed using the STRING platform The analysis reveals a dysregulation of the Signaling by Receptor Tyrosine Kinases pathway (proteins in red) and signal transduction (proteins in purple).\u003c/p\u003e","description":"","filename":"fig2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7109703/v1/e688fe8a5fe40877ea5e1b17.jpg"},{"id":90312538,"identity":"c8efec25-20ab-4fea-a857-f94c03628c4d","added_by":"auto","created_at":"2025-09-01 09:57:49","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":479228,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eIn vitro study of fibroblasts with serum of SSc patients from the distinctive clusters.\u003c/strong\u003e (A) Fibroblasts were treated with 15% serum from Cluster 1 and Cluster 2 patients for 24 hours to assess the impact of the molecular alterations characteristic of each SSc subgroup. (B) Expression levels of genes related to Fibrosis. (C) Expression levels of genes related to Inflammation.\u003c/p\u003e","description":"","filename":"fig3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7109703/v1/c8027ee6b40133584b7ce5d4.jpg"},{"id":90311346,"identity":"853a7d14-1bf1-4591-89f4-0fbc0beb002f","added_by":"auto","created_at":"2025-09-01 09:49:49","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":356307,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eProtein signatures associated with key clinical variables in SSc patients.\u003c/strong\u003e Boxplots depict statistically significant differences in protein expression across the entire SSc cohort, categorized according to distinct clinical variables: (A) anti-Scl70 positivity, (B) anti-centromere positivity, (C) pulmonary arterial hypertension (PAH), and (D) esophageal dysmotility.\u003c/p\u003e","description":"","filename":"fig4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7109703/v1/48862586f838be71959d4378.jpg"},{"id":97179610,"identity":"76b03cac-5740-4a64-9567-dcea5d1263d4","added_by":"auto","created_at":"2025-12-01 16:16:20","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":3083528,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7109703/v1/418af528-092e-449c-8802-7caa0cf1fd96.pdf"},{"id":90310125,"identity":"6e91b509-a0d8-4a87-8409-c4bcd50f9f05","added_by":"auto","created_at":"2025-09-01 09:41:49","extension":"docx","order_by":5,"title":"","display":"","copyAsset":false,"role":"supplement","size":42966,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryMethods.docx","url":"https://assets-eu.researchsquare.com/files/rs-7109703/v1/7a7532c3503fbb5f6deeab93.docx"},{"id":90310123,"identity":"2d93760a-d25f-4321-b47c-36b48cc898a8","added_by":"auto","created_at":"2025-09-01 09:41:49","extension":"docx","order_by":6,"title":"","display":"","copyAsset":false,"role":"supplement","size":36464,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryTables.docx","url":"https://assets-eu.researchsquare.com/files/rs-7109703/v1/c7fac8c664843f10ee20e89e.docx"}],"financialInterests":"","formattedTitle":"Deciphering Systemic Sclerosis Phenotypes: A Novel Approach Using Clustering Algorithms and Proteomic Insights. Results from the PRECISESADS Study.","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eSystemic sclerosis (SSc) is a rare systemic autoimmune disease characterized by progressive multiorgan involvement. Although its pathophysiology is not fully understood, key mechanisms include immune dysregulation, progressive fibrosis, and vasculopathy, mainly affecting the microvasculature\u003csup\u003e1\u003c/sup\u003e. Clinically, SSc exhibits significant heterogeneity, ranging from mild cutaneous or vascular manifestations to rapidly progressive multiorgan damage. Among autoimmune rheumatic diseases, it has the highest disease-specific mortality rate\u003csup\u003e2\u003c/sup\u003e, largely driven by disease progression and the lack of effective disease-modifying therapies\u003csup\u003e3\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eDespite advances in our understanding of SSc over the past decades, its mortality rate remains unacceptably high, with only marginal improvements observed over the last 40 years\u003csup\u003e4\u003c/sup\u003e. Several factors have been associated with increased mortality, including male sex, the diffuse cutaneous phenotype, and cumulative organ damage, such as interstitial lung disease (ILD) and pulmonary arterial hypertension (PAH)\u003csup\u003e5\u003c/sup\u003e. Tools like the Scleroderma Clinical Trials Consortium Damage Index (SCTC-DI) have shown prognostic value but remain limited in clinical use\u003csup\u003e6\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eAccurate stratification of SSc patients is crucial for the early identification of individuals at higher risk of developing organ damage, as well as for potential preventive interventions. Traditionally, SSc patients have been classified based on the extent of skin sclerosis (\u003cem\u003esine\u003c/em\u003e, limited, or diffuse) following the description by LeRoy et al. in 1988\u003csup\u003e7\u003c/sup\u003e. This classification gained widespread clinical acceptance due to its ease of use, requiring no additional tools beyond the Modified Rodnan Skin Score (mRSS)\u003csup\u003e8\u003c/sup\u003e, and it remains extensively used today. However, significant limitations of this classification have recently emerged\u003csup\u003e9\u003c/sup\u003e, as it may not adequately reflect cumulative organ damage in certain patient subgroups. Additional criticisms of this model include the dynamic nature of skin involvement, the high interobserver variability, and the poor prognosis observed in a subset of patients despite having a limited cutaneous phenotype\u003csup\u003e10\u003c/sup\u003e. Moreover, cutaneous involvement does not necessarily correlate with internal organ damage; patients with minimal skin sclerosis may still experience life-threatening complications\u003csup\u003e11\u003c/sup\u003e, underscoring the need for alternative classification methods.\u003c/p\u003e\u003cp\u003eSince autoimmunity is a key process in the pathophysiology of SSc, autoantibodies play a fundamental role in the disease. Antinuclear antibodies (ANAs) are present in over 90% of patients, though their absence does not exclude diagnosis\u003csup\u003e12\u003c/sup\u003e. A set of nearly mutually exclusive\u003csup\u003e13\u003c/sup\u003e SSc-specific autoantibodies (such as anti-Scl-70, anti-centromere (ACA), and anti-RNA polymerase III) has proven useful for patient stratification and is included in classification criteria\u003csup\u003e14\u003c/sup\u003e. ACA typically correlates with the limited cutaneous form and lower risk of severe organ involvement, whereas anti-Scl-70 and anti-RNA polymerase III, particularly the latter, are associated with diffuse skin involvement, more aggressive disease, and higher mortality.\u003c/p\u003e\u003cp\u003eAdditional autoantibodies, including non-specific ones like anti-Ro-52\u003csup\u003e15\u003c/sup\u003e, have been linked to complications such as ILD. Emerging markers like anti-eIF2B\u003csup\u003e16\u003c/sup\u003e have been identified in a subset of seronegative patients (~\u0026thinsp;10%), although their clinical relevance remains unclear.\u003c/p\u003e\u003cp\u003eGiven these findings, some authors have proposed an alternative classification model based on autoantibody profile rather than cutaneous phenotype\u003csup\u003e17\u003c/sup\u003e. This hypothesis was recently tested in a French cohort, demonstrating improved mortality prediction\u003csup\u003e18\u003c/sup\u003e. However, important gaps remain in risk stratification, as some patients with autoantibodies typically linked to milder phenotypes still experience poor outcomes\u003csup\u003e11\u003c/sup\u003e. While autoantibody-based models enhance risk prediction, they do not fully reflect disease heterogeneity, particularly in cases with overlapping features or undefined serological profiles. However, autoantibodies alone may not predict disease trajectory, since genetic, environmental, and epigenetic factors also contribute to progression\u003csup\u003e19\u003c/sup\u003e. These limitations highlight the need to refine phenotyping strategies.\u003c/p\u003e\u003cp\u003eThe increasing recognition of the complexity of SSc has driven the incorporation of molecular tools into patient stratification. Recent studies have demonstrated that integrating transcriptomic and proteomic profiling can identify distinct molecular signatures, enabling a more precise and personalized classification of the disease. In particular, proteomics has emerged as a promising tool for detecting key biomarkers associated with organ damage progression, facilitating the identification of subgroups with different prognoses and therapeutic responses\u003csup\u003e20,21\u003c/sup\u003e. These approaches represent a paradigm shift in SSc classification, moving away from models based solely on clinical and serological manifestations toward integrative systems that combine clinical, immunological, and molecular data. Importantly, such strategies also help overcome the limitations of current animal models, which often fail to reproduce the full spectrum of immune, vascular, and fibrotic manifestations observed in human SSc, thus bridging a critical gap between preclinical research and clinical application\u003csup\u003e22\u003c/sup\u003e. However, the clinical implementation of these findings remains challenging, as further validation is required to translate these advancements into routine medical practice.\u003c/p\u003e\u003cp\u003eThe objective of our study is to explore new approaches for the accurate stratification and characterization of SSc patients. We aim to achieve this by employing an unsupervised clustering algorithm and proteomic analysis to detect key protein expression differences pivotal for understanding the disease\u0026rsquo;s pathophysiology.\u003c/p\u003e"},{"header":"2. Methods","content":"\u003cp\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eStudy Design and Population\u003c/span\u003e\u003c/p\u003e\u003cp\u003eThis observational, cross-sectional study was conducted using the PRECISESADS cohort, a multicenter European initiative involving 19 centers across 9 countries (Austria, Belgium, France, Germany, Hungary, Italy, Portugal, Spain, and Switzerland). The cohort enrolled patients with systemic autoimmune diseases (SADs) aged over 18, recruited from 2014 to 2019. Biological samples and data from electronic medical records were collected from all patients. The study protocol is registered on ClinicalTrials.gov under the identifier NCT02890126\u003csup\u003e23\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eA total of 402 patients diagnosed with SSc who met the 2013 classification criteria of the American College of Rheumatology (ACR)/European Alliance of Associations for Rheumatology (EULAR)\u003csup\u003e14\u003c/sup\u003e were included in this study. In addition, an independent validation cohort comprising 213 patients with SSc was recruited at Reina Sofia University Hospital (Cordoba, Spain). This local cohort was used to replicate and validate key findings derived from the PRECISESADS dataset.\u003c/p\u003e\u003cp\u003eExclusion criteria comprised the concomitant diagnosis of another SAD, the use of a combination of two or more immunosuppressive agents, high-dose corticosteroid therapy in the three months prior to recruitment (\u0026ge;\u0026thinsp;15 mg/day), chronic infections, or participation in experimental therapies.\u003c/p\u003e\u003cp\u003e All participants were fully informed about the study objectives and provided written informed consent prior to inclusion. The study adhered to ethical standards and was approved by the local research ethics committees of the participating centers, including Reina Sofia University Hospital (approval number 5995).\u003c/p\u003e\u003cp\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eVariables and Clinical Data Collection\u003c/span\u003e\u003c/p\u003e\u003cp\u003eCommon variables recorded for all SADs included sex, age at onset, ethnicity, disease duration since diagnosis, and treatment. For SSc, a total of 87 clinically relevant variables were collected throughout the PRECISESADS project, from which a subset was selected for the present analysis (Supplementary Table\u0026nbsp;1). These included antibody status, cutaneous phenotype, and organ involvement (e.g., PAH, ILD, or esophageal dysmotility).\u003c/p\u003e\u003cp\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eClustering Analysis\u003c/span\u003e\u003c/p\u003e\u003cp\u003ePatients were stratified into distinct subgroups using an unsupervised k-means clustering algorithm applied to a curated selection of clinical and serological variables. Variable inclusion was guided by clinical expertise and their established relevance to key pathophysiological features of systemic sclerosis, prioritizing parameters associated with disease severity and major organ involvement. Only variables with complete data were included in the clustering analysis.\u003c/p\u003e\u003cp\u003eThe optimal number of clusters was determined using the NbClust package in R, which evaluates 23 internal validation indices. Among these, six supported a two-cluster solution, including the Calinski-Harabasz, Silhouette, Duda, Pseudo T\u0026sup2;, Beale, and McClain indices. This consensus supports the selection of k\u0026thinsp;=\u0026thinsp;2 as the most parsimonious and biologically coherent solution. Although alternative configurations (e.g., \u003cem\u003ek\u003c/em\u003e\u0026thinsp;=\u0026thinsp;3, 4, or 5) were also explored, they yielded less distinct or clinically non-informative partitions. This two-cluster solution was subsequently validated in the independent local cohort, confirming its robustness and clinical relevance.\u003c/p\u003e\u003cp\u003eFor the clustering analysis, the following R packages were utilized: stats (for k-means clustering), NbClust (for cluster validation), cluster (for silhouette width calculation), and factoextra (for visualization).\u003c/p\u003e\u003cp\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eProteomic Analysis\u003c/span\u003e\u003c/p\u003e\u003cp\u003eFollowing cluster identification, a representative subset of patients from each cluster within the original PRECISESADS cohort was randomly selected for proteomic profiling using OLINK\u0026reg; technology (Uppsala, Sweden), a high-throughput platform for multiplexed protein quantification. A panel of 92 proteins, selected for their relevance to organ damage, was analyzed. Differential protein expression between clusters was assessed using volcano plots and principal component analysis (PCA) to visualize clustering patterns and separation. A complete list of the 92 proteins analyzed is available in Supplementary Table\u0026nbsp;2.\u003c/p\u003e\u003cp\u003eStatistically significant proteins (with a controlled false discovery rate, FDR) were further evaluated using functional enrichment analyses via STRING-db.org to explore the implicated cellular pathways.\u003c/p\u003e\u003cp\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eIn Vitro Experiments\u003c/span\u003e\u003c/p\u003e\u003cp\u003eAn in vitro experiment was conducted using fibroblast cells, which play a key role in the pathogenesis of SSc.\u003c/p\u003e\u003cp\u003eDermal fibroblasts were immortalized in-house by overexpression of SV40, generating the human primary transformed cell line FibSV40. Cells were cultured in DMEM medium supplemented with 10% heat-inactivated fetal bovine serum (FBS) and 1% antibiotic-antimycotic solution, with medium replacement every 48 hours until 80% confluence was reached.\u003c/p\u003e\u003cp\u003eCultures were maintained in an incubator at 37\u0026deg;C with 5% CO₂ (Thermo Scientific, Series 8000 DH, Illinois, USA).\u003c/p\u003e\u003cp\u003eTo evaluate the impact of the altered circulating profiles in SSc, fibroblasts were treated for 24 hours with 15% serum from eighteen SSc patients (nine from each of the two previously identified molecular clusters matched for disease duration, age and sex). Following incubation, fibroblasts were washed three times with PBS before RNA extraction to eliminate extracellular nucleic acids and avoid serum-derived mRNA contamination. Subsequently, several markers of fibrosis and inflammation were analyzed using RT-qPCR.\u003c/p\u003e\u003cp\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eRNA Isolation and Transcriptomic Analyses\u003c/span\u003e\u003c/p\u003e\u003cp\u003eExpression levels of selected genes related to fibrosis and inflammation (including PDGFRB, WNT3A, MMP2, CCL3, TNFα, CCL5, and IL-1B) were analyzed in fibroblasts by quantitative real-time reverse transcription PCR (RT-qPCR).\u003c/p\u003e\u003cp\u003eRNA was extracted from fibroblast samples by homogenization in TRIzol\u0026trade; (Invitrogen), followed by phase separation with chloroform, as previously described\u003csup\u003e24\u003c/sup\u003e. Reverse transcription was performed using the PrimeScript RT Master Mix Kit (Takara, Lisbon, Portugal), according to the manufacturer\u0026rsquo;s instructions.\u003c/p\u003e\u003cp\u003eqPCR was carried out using a LightCycler thermocycler system (Roche Diagnostics, Indianapolis, USA) and SYBR\u0026reg; Green (Promega Biotech, Madrid, Spain). Primer sequences (Merck KGaA, Darmstadt, Germany) are listed in Supplementary Table\u0026nbsp;1.\u003c/p\u003e\u003cp\u003eGene expression levels were normalized using the geometric mean of α-actin (ACT) and glyceraldehyde-3-phosphate dehydrogenase (GAPDH). Data analysis was performed using the 2^-ΔΔCt method.\u003c/p\u003e\u003cp\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eStatistical Analysis\u003c/span\u003e\u003c/p\u003e\u003cp\u003eData were analyzed using RStudio v.2024.09. Descriptive statistics were reported as mean and standard deviation for continuous variables, and as frequencies and percentages for categorical variables. Group comparisons were performed using the Student\u0026rsquo;s t-test for continuous variables and the chi-square test for categorical variables.\u003c/p\u003e\u003cp\u003eA bidirectional approach was applied to all statistical models. The threshold for statistical significance was set at p\u0026thinsp;\u0026lt;\u0026thinsp;0.05, and the Benjamini-Hochberg procedure was applied to control the FDR due to multiple testing.\u003c/p\u003e"},{"header":"3. Results","content":"\u003cp\u003e\u003cspan type=\"BoldUnderline\" class=\"BoldUnderline\" name=\"Emphasis\"\u003eUnsupervised clustering analysis revealed two distinct clinical subgroups among SSc patients\u003c/span\u003e\u003c/p\u003e\u003cp\u003eA total of 402 patients from the PRECISESADS cohort were included in the analysis. The cohort was predominantly female (84.3%), with a mean age of 58.1\u0026thinsp;\u0026plusmn;\u0026thinsp;12.9 years and a mean disease duration of 10.8\u0026thinsp;\u0026plusmn;\u0026thinsp;8.7 years at enrollment.\u003c/p\u003e\u003cp\u003eAn unsupervised clustering algorithm applied to clinical and serological variables identified two distinct patient subgroups (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Cluster 1 (n\u0026thinsp;=\u0026thinsp;221) and Cluster 2 (n\u0026thinsp;=\u0026thinsp;181) differed significantly in clinical features, autoantibody profiles, and the extent of organ involvement, underscoring the heterogeneity of SSc.\u003c/p\u003e\u003cp\u003eCluster 2 exhibited a higher burden of severe organ involvement, with significantly increased prevalence of interstitial lung disease (ILD, 59.7% vs. 14.5%), pulmonary arterial hypertension (PAH, 30.9% vs. 9%), and sclerodactyly (93.9% vs. 53.4%) (all p\u0026thinsp;\u0026lt;\u0026thinsp;0.01). Musculoskeletal and vascular complications were also more frequent in Cluster 2, including arthritis (39.8% vs. 20.8%), calcinosis (43.1% vs. 6.8%), digital ulcers (15.5% vs. 5%), telangiectasias (83.4% vs. 39.8%), pitting scars (73.5% vs. 29.4%), and muscle weakness (28.2% vs. 9.5%) (p\u0026thinsp;\u0026lt;\u0026thinsp;0.01 for all). Limited cutaneous phenotype was more prevalent in Cluster 1 (60.2% vs. 45.9%, p\u0026thinsp;\u0026lt;\u0026thinsp;0.01), suggesting more extensive skin involvement in Cluster 2.\u003c/p\u003e\u003cp\u003eNo significant differences were found in age (59.2\u0026thinsp;\u0026plusmn;\u0026thinsp;12.4 vs. 57.2\u0026thinsp;\u0026plusmn;\u0026thinsp;13.2 years, p\u0026thinsp;=\u0026thinsp;0.12) or disease duration (11.6\u0026thinsp;\u0026plusmn;\u0026thinsp;7.5 vs. 10.2\u0026thinsp;\u0026plusmn;\u0026thinsp;9.6 years, p\u0026thinsp;=\u0026thinsp;0.10), indicating that the observed divergence reflects distinct disease phenotypes rather than demographic or temporal factors.\u003c/p\u003e\u003cp\u003eAutoantibody profiles also differentiated the clusters. Anti-Scl-70 antibodies were more frequent in Cluster 2 (38.7% vs. 19.9%, p\u0026thinsp;\u0026lt;\u0026thinsp;0.01), consistent with their association with severe disease. Conversely, anti-centromere antibodies were more prevalent in Cluster 1 (43.4% vs. 29.3%, p\u0026thinsp;\u0026lt;\u0026thinsp;0.01), though notably, around 30% of patients in Cluster 2 also expressed them, highlighting the limitations of classification based solely on autoantibodies.\u003c/p\u003e\u003cp\u003eThese findings were partially replicated in an independent cohort of 213 patients from Reina Sof\u0026iacute;a University Hospital (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). While some clinical patterns were preserved, differences in disease duration and organ involvement were observed. Thus, this analysis should be considered an exploratory replication supporting the generalizability of the clustering structure.\u003c/p\u003e\u003cp\u003e\u003cspan type=\"BoldUnderline\" class=\"BoldUnderline\" name=\"Emphasis\"\u003eThe identified clinical subgroups of SSc patients also displayed distinct patterns of proteins associated with organ damage.\u003c/span\u003e\u003c/p\u003e\u003cp\u003eTo explore the molecular basis underlying the clinical clusters, serum proteomic profiling was performed in a random subset of 154 patients (77 per cluster), using a panel of 92 organ damage-related proteins quantified by Proximity Extension Assay (PEA) technology. The selected patients mirrored the clinical features of each cluster.\u003c/p\u003e\u003cp\u003ePrincipal component analysis (PCA) revealed partial segregation of the two clusters based on proteomic profiles (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eA). Despite overall correlation across samples, distinct molecular patterns emerged (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eB). Cluster 2 exhibited upregulated levels of NOS3, PON2, MAP4K5, AIFM1, PRKAB1, NUB1, STX8, and PDGFC, and downregulation of HPGDS compared to Cluster 1 (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eC, D). These proteins are involved in inflammation, oxidative stress, endothelial dysfunction, metabolism, and fibrosis (hallmarks of SSc pathogenesis) and their dysregulation suggests a more aggressive molecular phenotype in Cluster 2.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003cspan type=\"BoldUnderline\" class=\"BoldUnderline\" name=\"Emphasis\"\u003eThe clinical cluster with the most severe phenotype exhibited the strongest molecular alterations at the protein level.\u003c/span\u003e\u003c/p\u003e\u003cp\u003eTo gain further insight into these molecular alterations, we conducted a comparative analysis of organ damage-related protein levels in the identified clusters versus 17 healthy donors, aiming to identify proteins that are altered in these patient groups. Compared to healthy controls, Cluster 1 showed upregulation of only eight proteins (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA), whereas Cluster 2 exhibited a markedly broader proteomic dysregulation, with 26 proteins upregulated (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eB). These findings further suggest that although molecular changes are present in all SSc patients, those in Cluster 2 exhibit a more pronounced proteomic signature that aligns with their more severe clinical manifestations.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eWe also examined the overlap of deregulated proteins in Cluster 1 and Cluster 2 compared to healthy donors. All eight proteins altered in Cluster 1 (PGF, RRM2B, YES1, ENAH, CLEC1A, NOS3, EGFL7, and SERPINA9) were also dysregulated in Cluster 2, suggesting a key role in SSc pathogenesis. Moreover, Cluster 2 exhibited altered expression of an additional 18 unique proteins (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eC). We also performed a pathway enrichment analysis of the altered protein signature in SSc to gain further insights. This analysis revealed an interactive protein network (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eD) primarily associated with receptor tyrosine Kinases signaling (RTK) (FDR\u0026thinsp;=\u0026thinsp;0.0091, signal 0.63) and broader signal transduction pathways (FDR\u0026thinsp;=\u0026thinsp;0.0091, signal 0.43) (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eD). The RTK pathway is critical in multiple biological processes, including fibrosis, immune activation, and endothelial dysfunction, all of which are key contributors to SSc pathogenesis. The dysregulation observed in Cluster 2 suggests that targeting this pathway could represent a potential therapeutic strategy for patients with severe disease phenotypes.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003cspan type=\"BoldUnderline\" class=\"BoldUnderline\" name=\"Emphasis\"\u003eSerum from the most severe clinical and molecular subgroup of SSc patients induced the expression of fibrotic and inflammatory genes in fibroblasts in vitro.\u003c/span\u003e\u003c/p\u003e\u003cp\u003eTo further explore the functional relevance of these proteomic alterations, an in vitro experiment was conducted using immortalized dermal fibroblasts.\u003c/p\u003e\u003cp\u003eThese fibroblasts were cultured in media containing serum from randomly selected patients from each cluster to assess their response to distinct molecular environments (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA). The objective was to determine whether the identified molecular differences translate into functional alterations in fibroblast activation.\u003c/p\u003e\u003cp\u003eSerum of patients from Cluster 2 promoted the upregulation of fibrotic-related genes including PDGFRB, WNT3A and MMP2 (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eB) as well as several genes associated with inflammation such as CCL3, TNF, CCL5 and IL1B (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eC).\u003c/p\u003e\u003cp\u003eThis experiment provided additional validation that the proteomic profiles observed in Cluster 2 are functionally relevant, influencing fibroblast behavior in a manner consistent with the more severe fibrotic and inflammatory phenotype observed in these patients.\u003c/p\u003e\u003cp\u003e\u003cspan type=\"BoldUnderline\" class=\"BoldUnderline\" name=\"Emphasis\"\u003eCirculating levels of specific organ damage-related proteins were directly associated with key clinical features in SSc patients.\u003c/span\u003e\u003c/p\u003e\u003cp\u003eIn addition to cluster-specific proteomic differences, a final exploratory analysis was performed across all patients (regardless of clustering) to assess associations between protein expression and specific clinical manifestations, aiming to identify novel disease biomarkers and potential therapeutic targets (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eAnti-Scl-70-positive patients exhibited increased levels of BANK1, BID, and ERBB2IP (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eA), reinforcing their link to immune activation and apoptosis. Anti-centromere-positive patients showed altered expression of ATP6AP2, LTA4H, and DPP6 (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eB), proteins involved in immune regulation and vascular homeostasis. Patients with pulmonary arterial hypertension (PAH) demonstrated elevated expression of AGR2, BAMBI, CALCA, EPO, and HPGDS (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eD), proteins implicated in pulmonary vascular remodeling, endothelial dysfunction, and hypoxia response. Similarly, esophageal dysmotility was associated with upregulation of proteins related to neuronal signaling and fibrosis, including FKBPLB and PRKAB1 (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eE), suggesting a potential role in neuromuscular dysfunction. All protein-clinical variable associations described were statistically significant.\u003c/p\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eOur study identifies two clinically and molecularly distinct subgroups of patients with SSc, providing a foundation for reclassifying this heterogeneous disease beyond traditional clinical phenotypes. One of the subgroups (Cluster 2) exhibits a significantly more severe phenotype characterized by extensive organ involvement, distinct autoantibody profiles, and a deeper and more complex proteomic dysregulation. These findings reinforce the growing need to transition from descriptive to mechanism-based classification systems in SSc.\u003c/p\u003e\u003cp\u003e\u003cspan type=\"BoldUnderline\" class=\"BoldUnderline\" name=\"Emphasis\"\u003eClinical and Biological Interpretation of the Identified Clusters\u003c/span\u003e\u003c/p\u003e\u003cp\u003e The unsupervised clustering approach, validated in an independent local cohort, robustly distinguished two subgroups of SSc patients with divergent clinical trajectories. Cluster 2, in particular, was characterized by a markedly increased burden of pulmonary (ILD and PAH), vascular (digital ulcers, telangiectasias), gastrointestinal (esophageal dysmotility), or musculoskeletal involvement (arthritis, calcinosis, muscle weakness), suggesting a more aggressive systemic disease phenotype.\u003c/p\u003e\u003cp\u003eInterestingly, these differences were not accounted for by age or disease duration, indicating that they are likely driven by intrinsic molecular mechanisms rather than temporal disease evolution. This observation supports the existence of distinct biological subtypes within SSc, reinforcing the concept that clinical heterogeneity arises from specific underlying pathophysiological programs.\u003c/p\u003e\u003cp\u003eThe proteomic profile of Cluster 2 further substantiates this interpretation. Patients in this subgroup exhibited upregulation of proteins implicated in fibrosis (e.g., PDGFC, PRKAB1)\u003csup\u003e25,26\u003c/sup\u003e, oxidative stress (e.g., PON2, MAP4K5)\u003csup\u003e27,28\u003c/sup\u003e, endothelial dysfunction (e.g., NOS3)\u003csup\u003e29\u003c/sup\u003e, and immune activation (e.g., AIFM1, NUB1)\u003csup\u003e30,31\u003c/sup\u003e. These pathways are well-established contributors to SSc pathogenesis, and their coordinated dysregulation in Cluster 2 highlights a molecularly active, pro-fibrotic and inflammatory disease state. In contrast, Cluster 1, while not devoid of disease manifestations, showed a more limited proteomic disturbance, consistent with a milder clinical phenotype.\u003c/p\u003e\u003cp\u003eMoreover, the clustering results support the concept that molecular heterogeneity in SSc may be rooted in early vascular dysfunction, as previously hypothesized. The interplay between endothelial injury, immune activation, and fibrotic remodeling appears central to the aggressive phenotype observed in Cluster 2.\u003c/p\u003e\u003cp\u003e\u003cspan type=\"BoldUnderline\" class=\"BoldUnderline\" name=\"Emphasis\"\u003eShared and Unique Biomarkers: Implications for Disease Stratification\u003c/span\u003e\u003c/p\u003e\u003cp\u003eA notable finding of our study is the identification of a core set of eight proteins commonly dysregulated in both clusters when compared to healthy controls. These include PGF, RRM2B, YES1, ENAH, CLEC1A, NOS3, EGFL7, and SERPINA9, and may represent a fundamental pathogenic axis in SSc. Their persistent alteration across patient subtypes suggests their potential utility as universal biomarkers of disease presence or activity.\u003c/p\u003e\u003cp\u003eIn contrast, 18 proteins uniquely upregulated in Cluster 2 provide insight into disease severity and progression. Among them, LTA4H stands out for its markedly increased expression and known role in promoting fibrosis through leukotriene B4 (LTB4) signaling, as supported by recent preclinical data\u003csup\u003e33\u003c/sup\u003e. In that study, elevated LTB4 levels were found in patients with SSc (particularly those with ILD or diffuse cutaneous forms) and blockade of the LTB4\u0026ndash;BLT1 axis significantly reduced fibrosis in murine models. Mechanistically, LTB4 promoted myofibroblast and endothelial-to-mesenchymal transitions via PI3K/Akt/mTOR activation, independently of TGF-β. This protein, along with PRKAB1, MAP4K5, and PDGFC, may link inflammation, vascular dysfunction, and fibroblast activation in SSc.\u003c/p\u003e\u003cp\u003e\u003cspan type=\"BoldUnderline\" class=\"BoldUnderline\" name=\"Emphasis\"\u003eFunctional Validation: From Proteomics to Pathogenic Mechanisms\u003c/span\u003e\u003c/p\u003e\u003cp\u003eTo determine whether the proteomic differences observed between clusters translated into functional consequences, we performed in vitro stimulation of dermal fibroblasts with serum from patients in each cluster. Fibroblasts exposed to serum from Cluster 2 patients exhibited increased expression of pro-fibrotic genes (e.g., PDGFRB, WNT3A, MMP2)\u003csup\u003e35,36\u003c/sup\u003e and inflammatory mediators (e.g., CCL3, TNF, CCL5, IL1B)\u003csup\u003e37,38\u003c/sup\u003e, confirming that the circulating molecular milieu in these individuals is capable of actively modulating fibroblast behavior.\u003c/p\u003e\u003cp\u003eThese findings functionally validate the proteomic alterations observed in Cluster 2, linking them to fibroblast activation and key mechanisms of tissue remodeling and damage. This reinforces the biological relevance of the identified protein signatures and suggests that they are not mere bystanders but active participants in the pathogenesis of the fibrotic phenotype.\u003c/p\u003e\u003cp\u003e\u003cspan type=\"BoldUnderline\" class=\"BoldUnderline\" name=\"Emphasis\"\u003eMechanistic Insights and Therapeutic Implications\u003c/span\u003e\u003c/p\u003e\u003cp\u003ePathway enrichment analysis revealed a significant involvement of receptor tyrosine kinase (RTK)-related signaling in the protein network upregulated in Cluster 2. RTKs regulate cellular growth, differentiation, and extracellular matrix production, and their dysregulation has been implicated in various fibrotic and autoimmune disorders\u003csup\u003e39\u003c/sup\u003e. The enrichment of RTK components supports the hypothesis that this axis may represent a convergent pathogenic mechanism in severe SSc, integrating inflammatory, vascular, and fibrotic cues.\u003c/p\u003e\u003cp\u003eIn addition, several dysregulated proteins in Cluster 2 (e.g., MAP4K5, PRKAB1, AIFM1) are linked to intracellular signaling pathways involved in inflammation and fibrosis. These findings highlight the therapeutic relevance of intracellular kinase inhibitors, such as JAK inhibitors, which modulate cytokine-driven pathways, including those downstream of type I interferons\u003csup\u003e40\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eThrough mechanisms such as TGF-β inhibition, suppression of myofibroblast proliferation, macrophage modulation, and epigenetic regulation, JAK inhibitors have been proposed for patients with fibrotic phenotypes\u003csup\u003e41\u003c/sup\u003e. While none are currently approved for SSc, emerging evidence supports their use in selected profiles, particularly those with ILD\u003csup\u003e42\u003c/sup\u003e. Future trials should incorporate molecular stratification at the design phase, as uniform recruitment may dilute efficacy signals in heterogeneous populations.\u003c/p\u003e\u003cp\u003eInterferons activate intracellular signaling via the JAK/STAT pathway\u003csup\u003e39\u003c/sup\u003e and have been extensively studied in SSc. Most patients exhibit an \u0026ldquo;interferon signature,\u0026rdquo; based on ISG overexpression in affected tissues\u003csup\u003e43,44\u003c/sup\u003e. Whether this signature is causal or reflective of disease remains unclear, but recent studies suggest it associates with more severe phenotypes\u003csup\u003e45\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eA phase I trial of anifrolumab, an anti-IFNAR1 antibody, showed reductions in interferon-inducible proteins in SSc patients\u003csup\u003e46\u003c/sup\u003e. A phase III trial is ongoing and may expand therapeutic options\u003csup\u003e47\u003c/sup\u003e. Given the prominent interferon-related activation in Cluster 2, this axis represents a promising target in patients with inflammatory molecular signatures.\u003c/p\u003e\u003cp\u003eGiven the prominent activation of immune-related and interferon-associated proteins in Cluster 2, targeting this axis may hold particular promise in the subset of patients characterized by active inflammatory molecular signatures.\u003c/p\u003e\u003cp\u003eAdditionally, Cluster 2 showed enrichment of proteins involved in apoptosis resistance and mitochondrial stress, including BID, AIFM1, and ERBB2IP\u003csup\u003e48\u003c/sup\u003e. These may contribute to persistence of activated myofibroblasts, a hallmark of fibrosis\u003csup\u003e49\u003c/sup\u003e. Therapies aimed at restoring apoptotic signaling or selectively targeting profibrotic fibroblast subsets warrant further exploration\u003csup\u003e50\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003e\u003cspan type=\"BoldUnderline\" class=\"BoldUnderline\" name=\"Emphasis\"\u003eToward Precision Medicine in SSc\u003c/span\u003e\u003c/p\u003e\u003cp\u003eThe clinical and molecular heterogeneity of SSc has historically posed challenges for therapeutic development and disease management. Our findings suggest that integration of proteomic data with clinical phenotyping can enhance disease stratification and inform personalized approaches to care. For example, the strong associations between specific protein profiles and organ manifestations (e.g., HPGDS and PAH; PRKAB1 and esophageal dysmotility) highlight the potential for using serum biomarkers to guide monitoring and predict complications.\u003c/p\u003e\u003cp\u003eMoreover, the ability to define biologically meaningful clusters opens avenues for tailored therapeutic decision-making. Patients in Cluster 2 may benefit from early, aggressive intervention and closer monitoring due to their elevated molecular activity, while those in Cluster 1 could follow a more conservative approach. This proteomics-driven stratification represents a step toward precision medicine in SSc.\u003c/p\u003e\u003cp\u003eTo our knowledge, this is the first study to perform unsupervised clustering of patients with SSc based exclusively on extended clinical data (beyond the limited/diffuse cutaneous classification) and to subsequently assess their molecular profiles. This strategy enabled the identification of patient subgroups that are not only clinically distinct but also show meaningful differences at the molecular level.\u003c/p\u003e\u003cp\u003eOur findings contribute to the growing body of evidence highlighting the limitations of the traditional binary classification, which fails to capture the full spectrum of clinical trajectories and therapeutic responses in SSc\u003csup\u003e9\u0026ndash;11\u003c/sup\u003e. A large multicenter analysis based on the EUSTAR cohort previously applied cluster analysis to clinical data and identified phenotypic subsets with prognostic relevance, yet without incorporating molecular data\u003csup\u003e51\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eIn parallel, molecular profiling studies (particularly transcriptomic analyses of skin biopsies) have defined subsets characterized by dominant fibrotic, interferon, or vascular signatures\u003csup\u003e21,52,53\u003c/sup\u003e, which often transcend conventional clinical phenotypes. Additionally, longitudinal studies have identified molecular features associated with early versus late disease\u003csup\u003e53\u003c/sup\u003e and with progression from preclinical to overt SSc\u003csup\u003e54\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eOur study bridges these lines of evidence by showing that clinically derived clusters also reflect distinct molecular programs. Notably, we provide functional validation: serum from patients in the more severe cluster induces fibrotic and inflammatory gene expression in dermal fibroblasts, indicating that these clusters represent active pathogenic states rather than statistical groupings, consistent with prior studies demonstrating that molecular profiles can correspond to biologically active and clinically relevant pathways\u003csup\u003e55\u003c/sup\u003e. Altogether, our results support the integration of clinical and molecular data to refine patient stratification and guide personalized therapeutic approaches in SSc.\u003c/p\u003e\u003cp\u003e\u003cspan type=\"BoldUnderline\" class=\"BoldUnderline\" name=\"Emphasis\"\u003eStudy Limitations and Future Directions\u003c/span\u003e\u003c/p\u003e\u003cp\u003eDespite robust findings, several limitations should be noted. First, the proteomic analysis was limited to a targeted panel of 92 proteins, potentially overlooking relevant pathways. Future studies should incorporate broader omics approaches (e.g., transcriptomics, metabolomics, single-cell analyses) to capture the full spectrum of disease heterogeneity. Second, external validation in larger and multi-ethnic cohorts is needed to ensure generalizability. Longitudinal studies will help assess the predictive value of identified markers for progression and therapeutic response. Importantly, integration of molecular stratification into prospective clinical trials is crucial to determine its utility in guiding treatment decisions. In vivo validation of key targets (e.g., LTA4H, PRKAB1, AIFM1) will be essential to translate discovery into therapy. Multidisciplinary collaboration and international efforts remain key to overcoming current barriers in SSc research and care.\u003c/p\u003e"},{"header":"5. Conclusion","content":"\u003cp\u003eAltogether, our findings support the existence of distinct clinical and molecular subtypes within SSc, with potential implications for diagnosis, prognosis, and targeted therapy. The integration of unsupervised clinical clustering with serum proteomics and functional validation offers a powerful framework to unravel the complexity of SSc. This approach paves the way toward biomarker-driven stratification and precision medicine in a disease that remains challenging to treat. Future efforts should focus on expanding molecular profiling, validating key markers, and translating these insights into actionable clinical tools.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cu\u003eCollaborators\u003c/u\u003e:\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePRECISESADS Clinical Consortium:\u003c/strong\u003e Lorenzo Beretta, Barbara Vigone, Jacques-Olivier Pers, Alain Saraux, Val\u0026eacute;rie Devauchelle-Pensec, Divi Cornec, Sandrine Jousse-Joulin, Bernard Lauwerys, Julie Ducreux, Anne-Lise Maudoux, Carlos Vasconcelos, Ana Tavares, Esmeralda Neves, Raquel Faria, Mariana Brand\u0026atilde;o, Ana Campar, Ant\u0026oacute;nio Marinho, F\u0026aacute;tima Farinha, Isabel Almeida, Miguel Angel Gonzalez-Gay Mantec\u0026oacute;n, Ricardo Blanco Alonso, Alfonso Corrales Mart\u0026iacute;nez, Ricard Cervera, Ignasi Rodr\u0026iacute;guez-Pint\u0026oacute;, Gerard Espinosa, Rik Lories, Ellen De Langhe, Nicolas Hunzelmann, Doreen Belz, Torsten Witte, Niklas Baerlecken, Georg Stummvoll, Michael Zauner, Michaela Lehner, Eduardo Collantes, Rafaela Ortega-Castro, Ma Angeles Aguirre-Zamorano, Alejandro Escudero-Contreras, Ma Carmen Castro-Villegas, Norberto Ortego, Mar\u0026iacute;a Concepci\u0026oacute;n Fern\u0026aacute;ndez Rold\u0026aacute;n, Enrique Raya, Inmaculada Jim\u0026eacute;nez Mole\u0026oacute;n, Enrique de Ramon, Isabel D\u0026iacute;az Quintero, Pier Luigi Meroni, Maria Gerosa, Tommaso Schioppo, Carolina Artusi, Carlo Chizzolini, Aleksandra Zuber, Donatienne Wynar, Laszl\u0026oacute; Kov\u0026aacute;cs, Attila Balog, Magdolna De\u0026aacute;k, M\u0026aacute;rta Bocskai, Sonja Dulic, Gabriella K\u0026aacute;d\u0026aacute;r, Falk Hiepe, Velia Gerl, Silvia Thiel, Manuel Rodriguez Maresca, Antonio L\u0026oacute;pez-Berrio, Roc\u0026iacute;o Aguilar-Quesada, H\u0026eacute;ctor Navarro-Linares\u003c/p\u003e\n\u003cp\u003e\u003cu\u003eFunding statement\u003c/u\u003e:\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThis manuscript was made possible thanks to the support of the Andalusian Foundation for Rheumatology through grant FAR-2024-001 (SCLEROMIC project).\u003c/p\u003e\n\u003cp\u003e\u003cu\u003eDisclosure of conflicts of interest\u003c/u\u003e:\u003c/p\u003e\n\u003cp\u003eThe authors declare no conflict of interest.\u003c/p\u003e\n\u003cp\u003e\u003cu\u003eAcknowledgements\u003c/u\u003e\u003c/p\u003e\n\u003cp\u003eWe sincerely thank Professor Eduardo Mu\u0026ntilde;oz\u0026rsquo;s laboratory at the Maimonides Biomedical Research Institute of Cordoba (IMIBIC) for kindly providing the fibroblast cultures, without which the \u003cem\u003ein vitro\u003c/em\u003e experiments would not have been possible.\u003c/p\u003e\n\u003cp\u003e\u003cu\u003eAuthors contribution\u003c/u\u003e:\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e*\u003c/strong\u003eThese authors share equal contribution.\u003c/p\u003e\n\u003cp\u003eThe authors confirm contribution to the paper as follows:\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSantiago Dans-Caballero\u003c/strong\u003e: Investigation. Writing \u0026ndash; Original Draft. \u003cstrong\u003eRafaela Ortega-Castro\u003c/strong\u003e: Writing \u0026ndash; Review \u0026amp; Editing. Resources. Conceptualization. Supervision. \u003cstrong\u003eBeatriz Vell\u0026oacute;n-Garc\u0026iacute;a:\u003c/strong\u003e Writing \u0026ndash; Review \u0026amp; Editing. Resources. \u003cstrong\u003eAlejandro Escudero-Contreras:\u003c/strong\u003e\u0026nbsp; Writing \u0026ndash; Review \u0026amp; Editing. Supervision.\u003cstrong\u003e\u0026nbsp; Chary L\u0026oacute;pez-Pedrera\u003c/strong\u003e: Writing \u0026ndash; Review \u0026amp; Editing. Resources. Conceptualization. Supervision. \u003cstrong\u003eCarlos P\u0026eacute;rez-S\u0026aacute;nchez:\u003c/strong\u003e Writing \u0026ndash; Review \u0026amp; Editing. Resources. Conceptualization. Supervision. \u003cstrong\u003eClementina L\u0026oacute;pez-Medina\u003c/strong\u003e: Writing \u0026ndash; Review \u0026amp; Editing. Resources. Conceptualization. Supervision.\u003c/p\u003e\u003cp\u003e\u003cb\u003eDeclaration of generative AI and AI-assisted technologies in the writing process\u003c/b\u003e\u003c/p\u003e\u003cp\u003eDuring the preparation of this work, the authors used ChatGPT (OpenAI, San Francisco, CA) in order to assist with language editing, style refinement, and structural suggestions for improved clarity and readability. After using this tool, the authors reviewed and edited the content as needed and take full responsibility for the content of the publication.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eVolkmann ER, Andr\u0026eacute;asson K, Smith V. Systemic sclerosis. Lancet. 2022 Nov 25;401:304\u0026ndash;18. doi: 10.1016/S0140-6736(22)01692-0.\u003c/li\u003e\n\u003cli\u003eBournia VK, Fragoulis GE, Mitrou P, Mathioudakis K, Tsolakidis A, Tektonidou MG, et al. All-cause mortality in systemic rheumatic diseases under treatment compared with the general population, 2015-2019. RMD Open. 2021 Nov;7(3):e001694. doi: 10.1136/rmdopen-2021-001694.\u003c/li\u003e\n\u003cli\u003eDel Galdo F, Lescoat A, Conaghan PG\u003cem\u003e,\u003c/em\u003e Bertoldo E, Čolić J, Santiago T et al. EULAR recommendations for the treatment of systemic sclerosis: 2023 update. Ann. Rheum. Dis. 2024 Oct. doi: 10.1136/ard-2024-226430.\u003c/li\u003e\n\u003cli\u003eElhai M, Meune C, Avouac J, Kahan A, Allanore Y. Trends in mortality in patients with systemic sclerosis over 40 years: a systematic review and meta-analysis of cohort studies. Rheumatology (Oxford). 2012 Jun;51(6):1017-26. doi: 10.1093/rheumatology/ker269.\u003c/li\u003e\n\u003cli\u003ePokeerbux MR, Giovannelli J, Dauchet L, Mouthon L, Agard C, Launay D, et al. Survival and prognosis factors in systemic sclerosis: data of a French multicenter cohort, systematic review, and meta-analysis of the literature. 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Clin Exp Rheumatol. 2020 May-Jun;38 Suppl 125(3):106-14.\u003c/li\u003e\n\u003cli\u003eCavazzana I, Vojinovic T, Airo\u0026rsquo; P, Fredi M, Ceribelli A, Pedretti E, et al. Systemic Sclerosis-Specific Antibodies: Novel and Classical Biomarkers. Clin Rev Allergy Immunol. 2022 Jun 18;64(3):412\u0026ndash;430. doi: 10.1007/s12016-022-08946-w.\u003c/li\u003e\n\u003cli\u003eHeijnen IAFM, Foocharoen C, Bannert B, Carreira PE, Caporali R, Smith V, et al. Clinical significance of coexisting antitopoisomerase I and anticentromere antibodies in patients with systemic sclerosis: a EUSTAR group-based study. Clin Exp Rheumatol. 2013 Mar-Apr;31(2 Suppl 76):96-102.\u003c/li\u003e\n\u003cli\u003evan den Hoogen F, Khanna D, Fransen J, Johnson SR, Baron M, Tyndall A, et al. 2013 classification criteria for systemic sclerosis: an American College of Rheumatology/European League Against Rheumatism collaborative initiative. Ann Rheum Dis. 2013 Nov;72(11):1747-55.\u003c/li\u003e\n\u003cli\u003eChan EKL. 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Ann Rheum Dis. 2023;82(12):1513\u0026ndash;22. doi:10.1136/ard-2022-223986.\u003c/li\u003e\n\u003cli\u003eBellocchi C, Beretta L, Wang X, Lyons MA, Marchini M, Lorini M, et al. Longitudinal global transcriptomic profiling of preclinical systemic sclerosis reveals molecular changes associated with disease progression. Rheumatology (Oxford). 2023;62(4):1662\u0026ndash;8. doi:10.1093/rheumatology/keac492.\u003c/li\u003e\n\u003cli\u003eWermuth PJ, Piera-Velazquez S, Jimenez SA. Exosomes isolated from serum of systemic sclerosis patients display alterations in their content of profibrotic and antifibrotic microRNA and induce a profibrotic phenotype in cultured normal dermal fibroblasts. Clin Exp Rheumatol. 2017;35 Suppl 106(4):21\u0026ndash;30.\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eComparison of clinical and serological characteristics between the two identified clusters of SSc patients (PRECISESADS cohort). \u0026para;: variables included in the clustering algorithm. ILD: interstitial lung disease; PAH: pulmonary arterial hypertension; GERD: gastroesophageal reflux disease; bDMARD: biologic disease-modifying antirheumatic drug.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"5\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVariable\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePRECISESADS cohort (n\u0026thinsp;=\u0026thinsp;402)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eCluster 1 (n\u0026thinsp;=\u0026thinsp;221)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eCluster 2 (n\u0026thinsp;=\u0026thinsp;181)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003ep-value\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e\u003cp\u003eDemographic variables\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAge (years), mean (SD)\u003csup\u003e\u0026para;\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e58.1 (12.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e57.2 (13.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e59.2 (12.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.12\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDisease duration (years), mean (SD)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e10.8 (8.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e10.2 (9.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e11,6 (7.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.10\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSex (female), n (%)\u003csup\u003e\u0026para;\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e339 (84.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e188 (85.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e151 (83.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.75\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e\u003cp\u003eClinical features\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRaynaud phenomenon, n (%)\u003csup\u003e\u0026para;\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e389 (96.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e209 (94.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e180 (99.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e0.01\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLimited cutaneous phenotype, n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e216 (53.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e133 (60.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e83 (45.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.01\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSclerodactyly, n (%)\u003csup\u003e\u0026para;\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e288 (71.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e118 (53.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e170 (93.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.01\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eILD, n (%)\u003csup\u003e\u0026para;\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e140 (34.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e32 (14.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e108 (59.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.01\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePAH, n (%)\u003csup\u003e\u0026para;\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e76 (18.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e20 (9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e56 (30.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.01\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDigital ulcers, n (%)\u003csup\u003e\u0026para;\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e39 (9.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e11 (5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e28 (15.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.01\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTelangiectasias, n (%)\u003csup\u003e\u0026para;\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e239 (59.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e88 (39.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e151 (83.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.01\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePitting scars, n (%)\u003csup\u003e\u0026para;\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e198 (49.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e65 (29.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e133 (73.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.01\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePuffy fingers, n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e225 (56)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e116 (52.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e109 (60.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.14\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCalcinosis, n (%)\u003csup\u003e\u0026para;\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e93 (23.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e15 (6.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e78 (43.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.01\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eArthritis, n (%)\u003csup\u003e\u0026para;\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e118 (29.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e46 (20.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e72 (39.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.01\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGERD, n (%)\u003csup\u003e\u0026para;\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e267 (66.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e117 (52.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e150 (82.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.01\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEsophageal dysmotility, n (%)\u003csup\u003e\u0026para;\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e200 (49.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e77 (63.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e123 (68)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.01\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMuscle weakness, n (%)\u003csup\u003e\u0026para;\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e72 (17.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e21 (9.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e51 (28.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.01\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCentromere positivity, n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e149 (37.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e96 (43.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e53 (29.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.01\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eScl-70 positivity, n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e114 (28.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e44 (19.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e70 (38.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.01\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e\u003cp\u003eComorbidities\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEver smoking, n (%)\u003csup\u003e\u0026para;\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e55 (13.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e32 (14.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e23 (1.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.71\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHypertension, n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e129 (32.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e72 (32.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e57 (31.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.90\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDyslipidemia, n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e98 (24.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e57 (25.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e41 (22.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.54\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eObesity, n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e39 (9.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e23 (10.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e16 (8.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.72\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e\u003cp\u003eTreatments\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCurrent bDMARD, n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e10 (2.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2 (0.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e8 (4.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e0.04\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCurrent immunosuppressants, n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e106 (26.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e32 (14.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e74 (40.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.01\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eStatins use, n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e84 (20.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e42 (19)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e42 (23.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.36\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eComparison of clinical and serological characteristics between the two identified clusters of SSc patients (Reina Sofia University Hospital cohort). ILD: interstitial lung disease; PAH: pulmonary arterial hypertension; GERD: gastroesophageal reflux disease.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"5\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVariable\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eLocal cohort (n\u0026thinsp;=\u0026thinsp;213)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eCluster 1\u003c/p\u003e\u003cp\u003e(n\u0026thinsp;=\u0026thinsp;92)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eCluster 2 (n\u0026thinsp;=\u0026thinsp;121)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003ep-valor\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e\u003cp\u003eDemographic variables\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAge (years), mean (SD)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e62.9 (14.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e61.8 (13.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e64.9 (15.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.01\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDisease duration (years), mean (SD)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e10.5 (8.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e8.3 (7.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e12.9 (8.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.01\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSex (female), n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e187 (87.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e81 (88)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e106 (87.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e\u003cp\u003eClinical features\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRaynaud phenomenon, n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e186 (87.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e78 (84.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e108 (89.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.13\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLimited cutaneous phenotype, n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e128 (60)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e54 (58.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e74 (61.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.42\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSclerodactyly, n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e150 (70.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e55 (59.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e95 (78.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.01\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eILD, n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e85 (39.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e35 (38)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e50 (41.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.73\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePAH, n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e43 (20.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e10 (10.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e33 (27.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.01\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDigital ulcers, n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e68 (31.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e22 (23.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e46 (38)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e0.04\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTelangiectasias, n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e134 (62.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e42 (45.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e92 (76)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.01\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePitting scars, n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e76 (35.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e30 (32.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e46 (38)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.50\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCalcinosis, n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e35 (16.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e12 (13)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e23 (19)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.33\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eArthritis, n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e45 (21.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e18 (19.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e27 (22.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.73\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGERD, n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e106 (49.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e42 (45.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e64 (52.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.33\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCentromere positivity, n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e120 (56.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e57 (62)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e63 (52.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.16\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eScl-70 positivity, n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e31 (14.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e10 (10.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e21 (17.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.25\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e\u003cp\u003eComorbidities\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEver smoking, n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e56 (26.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e26 (28.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e30 (24.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.85\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHypertension, n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e81 (38)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e31 (33.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e50 (41.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.32\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDyslipidemia, n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e69 (32.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e30 (32.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e39 (32.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eStatins use, n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e69 (32.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e28 (30.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e41 (33.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.68\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":true,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"journal-of-translational-medicine","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"jtrm","sideBox":"Learn more about [Journal of Translational Medicine](http://translational-medicine.biomedcentral.com)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/jtrm/default.aspx","title":"Journal of Translational Medicine","twitterHandle":"@BioMedCentral","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-7109703/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7109703/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e\u003cp\u003eSystemic sclerosis (SSc) is a heterogeneous autoimmune disease with high mortality driven by multiorgan involvement and limited therapeutic options. Traditional classifications based on skin involvement or serology are insufficient to capture disease complexity or predict outcomes accurately.\u003c/p\u003e\u003ch2\u003eObjective\u003c/h2\u003e\u003cp\u003eTo identify clinically and molecularly distinct subtypes of SSc using unsupervised clustering and proteomic profiling.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e\u003cp\u003eK means clustering was applied to clinical and serological data from 402 SSc patients in the PRECISESADS cohort. The resulting clusters were validated in an independent local cohort (n\u0026thinsp;=\u0026thinsp;213). To explore molecular differences, a random subset of 154 PRECISESADS patients underwent serum proteomic profiling using a panel of 92 organ damage\u0026ndash;related proteins. Functional relevance was further investigated by exposing dermal fibroblasts to patient serum and assessing gene expression.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e\u003cp\u003eTwo distinct clusters were identified and validated, differing in organ involvement and autoantibody profiles. Cluster 2 was associated with more severe disease, including higher prevalence of ILD, PAH, and musculoskeletal manifestations, and enriched in anti-Scl-70 antibodies. Proteomic analysis revealed upregulation of 26 proteins in Cluster 2, related to fibrosis, inflammation, and endothelial dysfunction. Serum from these patients induced the in vitro expression of pro-fibrotic and inflammatory genes in fibroblasts. Altered levels of several proteins also correlated with relevant clinical features, suggesting potential biomarker utility.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e\u003cp\u003eUnsupervised clustering and proteomic profiling reveal biologically distinct subgroups within SSc, beyond traditional clinical or serological classifications. Our findings support the integration of molecular tools into patient stratification strategies, paving the way toward personalized medicine in SSc.\u003c/p\u003e","manuscriptTitle":"Deciphering Systemic Sclerosis Phenotypes: A Novel Approach Using Clustering Algorithms and Proteomic Insights. Results from the PRECISESADS Study.","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-09-01 09:41:45","doi":"10.21203/rs.3.rs-7109703/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Major revision","date":"2025-09-12T17:17:39+00:00","index":"","fulltext":""},{"type":"reviewerAgreed","content":"","date":"2025-08-20T17:08:45+00:00","index":0,"fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-08-20T16:58:41+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-07-14T15:15:56+00:00","index":"","fulltext":""},{"type":"submitted","content":"Journal of Translational Medicine","date":"2025-07-12T14:21:06+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"journal-of-translational-medicine","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"jtrm","sideBox":"Learn more about [Journal of Translational Medicine](http://translational-medicine.biomedcentral.com)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/jtrm/default.aspx","title":"Journal of Translational Medicine","twitterHandle":"@BioMedCentral","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"d83bfe37-992f-474d-9617-85436c6e2af3","owner":[],"postedDate":"September 1st, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2025-12-01T16:12:08+00:00","versionOfRecord":{"articleIdentity":"rs-7109703","link":"https://doi.org/10.1186/s12967-025-07469-0","journal":{"identity":"journal-of-translational-medicine","isVorOnly":false,"title":"Journal of Translational Medicine"},"publishedOn":"2025-11-28 15:58:36","publishedOnDateReadable":"November 28th, 2025"},"versionCreatedAt":"2025-09-01 09:41:45","video":"","vorDoi":"10.1186/s12967-025-07469-0","vorDoiUrl":"https://doi.org/10.1186/s12967-025-07469-0","workflowStages":[]},"version":"v1","identity":"rs-7109703","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7109703","identity":"rs-7109703","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}